Despite the surge of deep learning, deploying the deep learning-based pedestrian detection into the real system faces hurdles, mainly due to the huge resource usages. The classical feature-based detection system still becomes feasible option. There have been many efforts to improve the performance of pedestrian detection system. Among many feature set, Histogram of Oriented Gradient seems to be very effective for person detection. In this research, various machine learning algorithms are investigated for person detection. Different machine learning algorithms are evaluated to obtain the optimal accuracy and speed of the system.
Multimodal Biometrics Recognition by Dimensionality Diminution MethodIJERA Editor
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
Multilinear Kernel Mapping for Feature Dimension Reduction in Content Based M...ijma
In the process of content-based multimedia retrieval, multimedia information is processed in order to
obtain descriptive features. Descriptive representation of features, results in a huge feature count, which
results in processing overhead. To reduce this descriptive feature overhead, various approaches have been
used to dimensional reduction, among which PCA and LDA are the most used methods. However, these
methods do not reflect the significance of feature content in terms of inter-relation among all dataset
features. To achieve a dimension reduction based on histogram transformation, features with low
significance can be eliminated. In this paper, we propose a feature dimensional reduction approaches to
achieve the dimension reduction approach based on a multi-linear kernel (MLK) modeling. A benchmark
dataset for the experimental work is taken and the proposed work is observed to be improved in analysis in
comparison to the conventional system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Neural Network Approach to Identify Hyperspectral Image Content IJECEIAES
A Hyperspectral is the imaging technique that contains very large dimension data with the hundreds of channels. Meanwhile, the Hyperspectral Images (HISs) delivers the complete knowledge of imaging; therefore applying a classification algorithm is very important tool for practical uses. The HSIs are always having a large number of correlated and redundant feature, which causes the decrement in the classification accuracy; moreover, the features redundancy come up with some extra burden of computation that without adding any beneficial information to the classification accuracy. In this study, an unsupervised based Band Selection Algorithm (BSA) is considered with the Linear Projection (LP) that depends upon the metric-band similarities. Afterwards Monogenetic Binary Feature (MBF) has consider to perform the „texture analysis‟ of the HSI, where three operational component represents the monogenetic signal such as; phase, amplitude and orientation. In post processing classification stage, feature-mapping function can provide important information, which help to adopt the Kernel based Neural Network (KNN) to optimize the generalization ability. However, an alternative method of multiclass application can be adopt through KNN, if we consider the multi-output nodes instead of taking single-output node.
OBJECT DETECTION, EXTRACTION AND CLASSIFICATION USING IMAGE PROCESSING TECHNIQUEJournal For Research
Domestic refrigerators are widely used household appliances and a large extent of energy is consumed by this system. A phase change material is a substances that can store or release significant amount of heat energy by changing the phase liquid to vapour or vice versa. So, reduction of temperature fluctuation and improvement of system performances is that main reason of using PCM enhances the heat transfer rate thus improves the COP of refrigeration as well as the quality frozen food. The release and storage rate of a refrigerator is depends upon the characteristics of refrigerators and its properties using phase change material for a certain thermal load it is found that COP of conventional refrigerator is increased . The phase change material used in chamber built manually and which surrounds the evaporator chamber of a conventional refrigerator the whole heat transfer for load given to refrigerator cabin (to evaporator) evaporator to phase change material by conduction. This system hence improves the performances of household refrigerator by increasing its compressor cut-off time and thereby minimizing electrical energy usage. The main objective is to improve the performance, cooling time period, storage capacity and to maintain the constant cooling effect for more time during power cut off hours using phase change material.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Multimodal Biometrics Recognition by Dimensionality Diminution MethodIJERA Editor
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
Multilinear Kernel Mapping for Feature Dimension Reduction in Content Based M...ijma
In the process of content-based multimedia retrieval, multimedia information is processed in order to
obtain descriptive features. Descriptive representation of features, results in a huge feature count, which
results in processing overhead. To reduce this descriptive feature overhead, various approaches have been
used to dimensional reduction, among which PCA and LDA are the most used methods. However, these
methods do not reflect the significance of feature content in terms of inter-relation among all dataset
features. To achieve a dimension reduction based on histogram transformation, features with low
significance can be eliminated. In this paper, we propose a feature dimensional reduction approaches to
achieve the dimension reduction approach based on a multi-linear kernel (MLK) modeling. A benchmark
dataset for the experimental work is taken and the proposed work is observed to be improved in analysis in
comparison to the conventional system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Neural Network Approach to Identify Hyperspectral Image Content IJECEIAES
A Hyperspectral is the imaging technique that contains very large dimension data with the hundreds of channels. Meanwhile, the Hyperspectral Images (HISs) delivers the complete knowledge of imaging; therefore applying a classification algorithm is very important tool for practical uses. The HSIs are always having a large number of correlated and redundant feature, which causes the decrement in the classification accuracy; moreover, the features redundancy come up with some extra burden of computation that without adding any beneficial information to the classification accuracy. In this study, an unsupervised based Band Selection Algorithm (BSA) is considered with the Linear Projection (LP) that depends upon the metric-band similarities. Afterwards Monogenetic Binary Feature (MBF) has consider to perform the „texture analysis‟ of the HSI, where three operational component represents the monogenetic signal such as; phase, amplitude and orientation. In post processing classification stage, feature-mapping function can provide important information, which help to adopt the Kernel based Neural Network (KNN) to optimize the generalization ability. However, an alternative method of multiclass application can be adopt through KNN, if we consider the multi-output nodes instead of taking single-output node.
OBJECT DETECTION, EXTRACTION AND CLASSIFICATION USING IMAGE PROCESSING TECHNIQUEJournal For Research
Domestic refrigerators are widely used household appliances and a large extent of energy is consumed by this system. A phase change material is a substances that can store or release significant amount of heat energy by changing the phase liquid to vapour or vice versa. So, reduction of temperature fluctuation and improvement of system performances is that main reason of using PCM enhances the heat transfer rate thus improves the COP of refrigeration as well as the quality frozen food. The release and storage rate of a refrigerator is depends upon the characteristics of refrigerators and its properties using phase change material for a certain thermal load it is found that COP of conventional refrigerator is increased . The phase change material used in chamber built manually and which surrounds the evaporator chamber of a conventional refrigerator the whole heat transfer for load given to refrigerator cabin (to evaporator) evaporator to phase change material by conduction. This system hence improves the performances of household refrigerator by increasing its compressor cut-off time and thereby minimizing electrical energy usage. The main objective is to improve the performance, cooling time period, storage capacity and to maintain the constant cooling effect for more time during power cut off hours using phase change material.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Multi fractal analysis of human brain mr imageeSAT Journals
Abstract In computer programming, code smell may origin of latent problems in source code. Detecting and resolving bad smells remain time intense for software engineers despite proposals on bad smell detecting and refactoring tools. Numerous code smells have been recognized yet the sequence in which the detection and resolution of different kinds of code smells are performed because software engineers do not know how to optimize sequence. In this paper, the novel refactoring approach is proposed to improve the performance of programs. In this recommended approach the code smells are automatically detected and refactored. The simulation results propose the reduction of time over the semi-automated refactoring are achieved when code smells are refactored by using multi-step automated refactoring. Keywords: Code smell, multi step refactoring, detection, code resolution, restructuring etc
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITYIAEME Publication
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
Textural Feature Extraction of Natural Objects for Image ClassificationCSCJournals
The field of digital image processing has been growing in scope in the recent years. A digital image is represented as a two-dimensional array of pixels, where each pixel has the intensity and location information. Analysis of digital images involves extraction of meaningful information from them, based on certain requirements. Digital Image Analysis requires the extraction of features, transforms the data in the high-dimensional space to a space of fewer dimensions. Feature vectors are n-dimensional vectors of numerical features used to represent an object. We have used Haralick features to classify various images using different classification algorithms like Support Vector Machines (SVM), Logistic Classifier, Random Forests Multi Layer Perception and Naïve Bayes Classifier. Then we used cross validation to assess how well a classifier works for a generalized data set, as compared to the classifications obtained during training.
Multiclass Recognition with Multiple Feature Treescsandit
This paper proposes a multiclass recognition scheme which uses multiple feature trees with an
extended scoring method evolved from TF-IDF. Feature trees consisting of different feature
descriptors such as SIFT and SURF are built by the hierarchical k-means algorithm. The
experimental results show that the proposed scoring method combing with the proposed
multiple feature trees yields high accuracy for multiclass recognition and achieves significant
improvement compared to methods using a single feature tree with original TF-IDF.
This article aims at a new algorithm for tracking moving objects in the long term. We have tried to overcome some potential difficulties, first by a comparative study of the measuring methods of the difference and the similarity between the template and the source image. In the second part, an improvement of the best method allows us to follow the target in a robust way. This method also allows us to effectively overcome the problems of geometric deformation, partial occlusion and recovery after the target leaves the field of vision. The originality of our algorithm is based on a new model, which does not depend on a probabilistic process and does not require a data based detection in advance. Experimental results on several difficult video sequences have proven performance advantages over many recent trackers. The developed algorithm can be employed in several applications such as video surveillance, active vision or industrial visual servoing.
MULTIPLE HUMAN TRACKING USING RETINANET FEATURES, SIAMESE NEURAL NETWORK, AND...IAEME Publication
Multiple human tracking based on object detection has been a challenge due to its
complexity. Errors in object detection would be propagated to tracking errors. In this
paper, we propose a tracking method that minimizes the error produced by object
detector. We use RetinaNet as object detector and Hungarian algorithm for tracking.
The cost matrix for Hungarian algorithm is calculated using the RetinaNet features,
bounding box center distances, and intersection of unions of bounding boxes. We
interpolate the missing detections in the last step. The proposed method yield 43.2
MOTA for MOT16 benchmark
Proposed algorithm for image classification using regression-based pre-proces...IJECEIAES
Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics.
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...CSCJournals
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
Q UANTUM C LUSTERING -B ASED F EATURE SUBSET S ELECTION FOR MAMMOGRAPHIC I...ijcsit
In this paper, we present an algorithm for feature selection. This algorithm labeled QC-FS: Quantum
Clustering for Feature Selection performs the selection in two steps. Partitioning the original features
space in order to group similar features is performed using the Quantum Clustering algorithm. Then the
selection of a representative for each cluster is carried out. It uses similarity measures such as correlation
coefficient (CC) and the mutual information (MI). The feature which maximizes this information is chosen
by the algorithm
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient human–computer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
digital images
Control chart pattern recognition using k mica clustering and neural networksISA Interchange
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
An Automatic Medical Image Segmentation using Teaching Learning Based Optimiz...idescitation
Nature inspired population based evolutionary algorithms are very popular with
their competitive solutions for a wide variety of applications. Teaching Learning based
Optimization (TLBO) is a very recent population based evolutionary algorithm evolved
on the basis of Teaching Learning process of a class room. TLBO does not require any
algorithmic specific parameters. This paper proposes an automatic grouping of pixels into
different homogeneous regions using the TLBO. The experimental results have
demonstrated the effectiveness of TLBO in image segmentation.
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
This paper discusses various experiments conducted on different types of Level Sets interactive segmentation techniques using Matlab software, on select images. The objective is to assess the effectiveness on specific natural images, which have complex image composition in terms of intensity, colour mix, indistinct object boundary, low contrast, etc. Besides visual assessment, measures such as Jaccard Index, Dice Coefficient and Hausdorrf Distance have been computed to assess the accuracy of these techniques, between segmented and ground truth images. This paper particularly discusses Sparse Field Matrix and Localized Region Based Active Contours, both based on Level Sets. These techniques were not found to be effective where object boundary is not very distinct and/or has low contrast with background. Also, the techniques were ineffective on such images where foreground object stretches up to the image boundary.
Cartoon Based Image Retrieval : An Indexing Approachmlaij
This paper proposes a methodology for the content based image retrieval which is implemented on the
cartoon images. The similarities between a query cartoon character image and the images in database are
computed by the feature extraction using the fusion descriptors of SIFT (Scale Invariant Feature
Transforms) and HOG (Histogram of Gradient). Based on the similarities, the cartoon images same or
similar to query images are identified and retrieved. This method makes use of indexing technique for more
efficient and scalable retrieval of the cartoon character. The experiment results demonstrate that the
proposed method is efficient in retrieving the cartoon images from the large database.
Steganalysis of LSB Embedded Images Using Gray Level Co-Occurrence MatrixCSCJournals
This paper proposes a steganalysis technique for both grayscale and color images. It uses the feature vectors derived from gray level co-occurrence matrix (GLCM) in spatial domain, which is sensitive to data embedding process. This GLCM matrix is derived from an image. Several combinations of diagonal elements of GLCM are considered as features. There is difference between the features of stego and non-stego images and this characteristic is used for steganalysis. Distance measures like Absolute distance, Euclidean distance and Normalized Euclidean distance are used for classification. Experimental results demonstrate that the proposed scheme outperforms the existing steganalysis techniques in attacking LSB steganographic schemes applied to spatial domain.
A new model for iris data set classification based on linear support vector m...IJECEIAES
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
Improved feature selection using a hybrid side-blotched lizard algorithm and ...IJECEIAES
Feature selection entails choosing the significant features among a wide collection of original features that are essential for predicting test data using a classifier. Feature selection is commonly used in various applications, such as bioinformatics, data mining, and the analysis of written texts, where the dataset contains tens or hundreds of thousands of features, making it difficult to analyze such a large feature set. Removing irrelevant features improves the predictor performance, making it more accurate and cost-effective. In this research, a novel hybrid technique is presented for feature selection that aims to enhance classification accuracy. A hybrid binary version of sideblotched lizard algorithm (SBLA) with genetic algorithm (GA), namely SBLAGA, which combines the strengths of both algorithms is proposed. We use a sigmoid function to adapt the continuous variables values into a binary one, and evaluate our proposed algorithm on twenty-three standard benchmark datasets. Average classification accuracy, average number of selected features and average fitness value were the evaluation criteria. According to the experimental results, SBLAGA demonstrated superior performance compared to SBLA and GA with regards to these criteria. We further compare SBLAGA with four wrapper feature selection methods that are widely used in the literature, and find it to be more efficient.
Multi fractal analysis of human brain mr imageeSAT Journals
Abstract In computer programming, code smell may origin of latent problems in source code. Detecting and resolving bad smells remain time intense for software engineers despite proposals on bad smell detecting and refactoring tools. Numerous code smells have been recognized yet the sequence in which the detection and resolution of different kinds of code smells are performed because software engineers do not know how to optimize sequence. In this paper, the novel refactoring approach is proposed to improve the performance of programs. In this recommended approach the code smells are automatically detected and refactored. The simulation results propose the reduction of time over the semi-automated refactoring are achieved when code smells are refactored by using multi-step automated refactoring. Keywords: Code smell, multi step refactoring, detection, code resolution, restructuring etc
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITYIAEME Publication
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
Textural Feature Extraction of Natural Objects for Image ClassificationCSCJournals
The field of digital image processing has been growing in scope in the recent years. A digital image is represented as a two-dimensional array of pixels, where each pixel has the intensity and location information. Analysis of digital images involves extraction of meaningful information from them, based on certain requirements. Digital Image Analysis requires the extraction of features, transforms the data in the high-dimensional space to a space of fewer dimensions. Feature vectors are n-dimensional vectors of numerical features used to represent an object. We have used Haralick features to classify various images using different classification algorithms like Support Vector Machines (SVM), Logistic Classifier, Random Forests Multi Layer Perception and Naïve Bayes Classifier. Then we used cross validation to assess how well a classifier works for a generalized data set, as compared to the classifications obtained during training.
Multiclass Recognition with Multiple Feature Treescsandit
This paper proposes a multiclass recognition scheme which uses multiple feature trees with an
extended scoring method evolved from TF-IDF. Feature trees consisting of different feature
descriptors such as SIFT and SURF are built by the hierarchical k-means algorithm. The
experimental results show that the proposed scoring method combing with the proposed
multiple feature trees yields high accuracy for multiclass recognition and achieves significant
improvement compared to methods using a single feature tree with original TF-IDF.
This article aims at a new algorithm for tracking moving objects in the long term. We have tried to overcome some potential difficulties, first by a comparative study of the measuring methods of the difference and the similarity between the template and the source image. In the second part, an improvement of the best method allows us to follow the target in a robust way. This method also allows us to effectively overcome the problems of geometric deformation, partial occlusion and recovery after the target leaves the field of vision. The originality of our algorithm is based on a new model, which does not depend on a probabilistic process and does not require a data based detection in advance. Experimental results on several difficult video sequences have proven performance advantages over many recent trackers. The developed algorithm can be employed in several applications such as video surveillance, active vision or industrial visual servoing.
MULTIPLE HUMAN TRACKING USING RETINANET FEATURES, SIAMESE NEURAL NETWORK, AND...IAEME Publication
Multiple human tracking based on object detection has been a challenge due to its
complexity. Errors in object detection would be propagated to tracking errors. In this
paper, we propose a tracking method that minimizes the error produced by object
detector. We use RetinaNet as object detector and Hungarian algorithm for tracking.
The cost matrix for Hungarian algorithm is calculated using the RetinaNet features,
bounding box center distances, and intersection of unions of bounding boxes. We
interpolate the missing detections in the last step. The proposed method yield 43.2
MOTA for MOT16 benchmark
Proposed algorithm for image classification using regression-based pre-proces...IJECEIAES
Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics.
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...CSCJournals
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
Q UANTUM C LUSTERING -B ASED F EATURE SUBSET S ELECTION FOR MAMMOGRAPHIC I...ijcsit
In this paper, we present an algorithm for feature selection. This algorithm labeled QC-FS: Quantum
Clustering for Feature Selection performs the selection in two steps. Partitioning the original features
space in order to group similar features is performed using the Quantum Clustering algorithm. Then the
selection of a representative for each cluster is carried out. It uses similarity measures such as correlation
coefficient (CC) and the mutual information (MI). The feature which maximizes this information is chosen
by the algorithm
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient human–computer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
digital images
Control chart pattern recognition using k mica clustering and neural networksISA Interchange
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
An Automatic Medical Image Segmentation using Teaching Learning Based Optimiz...idescitation
Nature inspired population based evolutionary algorithms are very popular with
their competitive solutions for a wide variety of applications. Teaching Learning based
Optimization (TLBO) is a very recent population based evolutionary algorithm evolved
on the basis of Teaching Learning process of a class room. TLBO does not require any
algorithmic specific parameters. This paper proposes an automatic grouping of pixels into
different homogeneous regions using the TLBO. The experimental results have
demonstrated the effectiveness of TLBO in image segmentation.
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
This paper discusses various experiments conducted on different types of Level Sets interactive segmentation techniques using Matlab software, on select images. The objective is to assess the effectiveness on specific natural images, which have complex image composition in terms of intensity, colour mix, indistinct object boundary, low contrast, etc. Besides visual assessment, measures such as Jaccard Index, Dice Coefficient and Hausdorrf Distance have been computed to assess the accuracy of these techniques, between segmented and ground truth images. This paper particularly discusses Sparse Field Matrix and Localized Region Based Active Contours, both based on Level Sets. These techniques were not found to be effective where object boundary is not very distinct and/or has low contrast with background. Also, the techniques were ineffective on such images where foreground object stretches up to the image boundary.
Cartoon Based Image Retrieval : An Indexing Approachmlaij
This paper proposes a methodology for the content based image retrieval which is implemented on the
cartoon images. The similarities between a query cartoon character image and the images in database are
computed by the feature extraction using the fusion descriptors of SIFT (Scale Invariant Feature
Transforms) and HOG (Histogram of Gradient). Based on the similarities, the cartoon images same or
similar to query images are identified and retrieved. This method makes use of indexing technique for more
efficient and scalable retrieval of the cartoon character. The experiment results demonstrate that the
proposed method is efficient in retrieving the cartoon images from the large database.
Steganalysis of LSB Embedded Images Using Gray Level Co-Occurrence MatrixCSCJournals
This paper proposes a steganalysis technique for both grayscale and color images. It uses the feature vectors derived from gray level co-occurrence matrix (GLCM) in spatial domain, which is sensitive to data embedding process. This GLCM matrix is derived from an image. Several combinations of diagonal elements of GLCM are considered as features. There is difference between the features of stego and non-stego images and this characteristic is used for steganalysis. Distance measures like Absolute distance, Euclidean distance and Normalized Euclidean distance are used for classification. Experimental results demonstrate that the proposed scheme outperforms the existing steganalysis techniques in attacking LSB steganographic schemes applied to spatial domain.
A new model for iris data set classification based on linear support vector m...IJECEIAES
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
Improved feature selection using a hybrid side-blotched lizard algorithm and ...IJECEIAES
Feature selection entails choosing the significant features among a wide collection of original features that are essential for predicting test data using a classifier. Feature selection is commonly used in various applications, such as bioinformatics, data mining, and the analysis of written texts, where the dataset contains tens or hundreds of thousands of features, making it difficult to analyze such a large feature set. Removing irrelevant features improves the predictor performance, making it more accurate and cost-effective. In this research, a novel hybrid technique is presented for feature selection that aims to enhance classification accuracy. A hybrid binary version of sideblotched lizard algorithm (SBLA) with genetic algorithm (GA), namely SBLAGA, which combines the strengths of both algorithms is proposed. We use a sigmoid function to adapt the continuous variables values into a binary one, and evaluate our proposed algorithm on twenty-three standard benchmark datasets. Average classification accuracy, average number of selected features and average fitness value were the evaluation criteria. According to the experimental results, SBLAGA demonstrated superior performance compared to SBLA and GA with regards to these criteria. We further compare SBLAGA with four wrapper feature selection methods that are widely used in the literature, and find it to be more efficient.
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
Palm print recognition based on harmony search algorithm IJECEIAES
Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Comparative study of optimization algorithms on convolutional network for aut...IJECEIAES
The last 10 years have been the decade of autonomous vehicles. Advances in intelligent sensors and control schemes have shown the possibility of real applications.
Deep learning, and in particular convolutional networks have become a fundamental
tool in the solution of problems related to environment identification, path planning,
vehicle behavior, and motion control. In this paper, we perform a comparative study of
the most used optimization strategies on the convolutional architecture residual neural network (ResNet) for an autonomous driving problem as a previous step to the
development of an intelligent sensor. This sensor, part of our research in reactive
systems for autonomous vehicles, aims to become a system for direct mapping of sensory information to control actions from real-time images of the environment. The
optimization techniques analyzed include stochastic gradient descent (SGD), adaptive gradient (Adagrad), adaptive learning rate (Adadelta), root mean square propagation (RMSProp), Adamax, adaptive moment estimation (Adam), nesterov-accelerated
adaptive moment estimation (Nadam), and follow the regularized leader (Ftrl). The
training of the deep model is evaluated in terms of convergence, accuracy, recall, and
F1-score metrics. Preliminary results show a better performance of the deep network
when using the SGD function as an optimizer, while the Ftrl function presents the
poorest performances.
Automatic Feature Subset Selection using Genetic Algorithm for Clusteringidescitation
Feature subset selection is a process of selecting a
subset of minimal, relevant features and is a pre processing
technique for a wide variety of applications. High dimensional
data clustering is a challenging task in data mining. Reduced
set of features helps to make the patterns easier to understand.
Reduced set of features are more significant if they are
application specific. Almost all existing feature subset
selection algorithms are not automatic and are not application
specific. This paper made an attempt to find the feature subset
for optimal clusters while clustering. The proposed Automatic
Feature Subset Selection using Genetic Algorithm (AFSGA)
identifies the required features automatically and reduces
the computational cost in determining good clusters. The
performance of AFSGA is tested using public and synthetic
datasets with varying dimensionality. Experimental results
have shown the improved efficacy of the algorithm with optimal
clusters and computational cost.
Self scale estimation of the tracking window merged with adaptive particle fi...IJECEIAES
Tracking a mobile object is one of the important topics in pattern recognition, but style has some obstacles. A reliable tracking system must adjust their tracking windows in real time according to appearance changes of the tracked object. Furthermore, it has to deal with many challenges when one or multiple objects need to be tracked, for instance when the target is partially or fully occluded, background clutter, or even some target region is blurred. In this paper, we will present a novel approach for a single object tracking that combines particle filter algorithm and kernel distribution that update its tracking window according to object scale changes, whose name is multi-scale adaptive particle filter tracker. We will demonstrate that the use of particle filter combined with kernel distribution inside the resampling process will provide more accurate object localization within a research area. Furthermore, its average error for target localization was significantly lower than 21.37 pixels as the mean value. We have conducted several experiments on real video sequences and compared acquired results to other existing state of the art trackers to demonstrate the effectiveness of the multi-scale adaptive particle filter tracker.
Wearable sensor-based human activity recognition with ensemble learning: a co...IJECEIAES
The spectacular growth of wearable sensors has provided a key contribution to the field of human activity recognition. Due to its effective and versatile usage and application in various fields such as smart homes and medical areas, human activity recognition has always been an appealing research topic in artificial intelligence. From this perspective, there are a lot of existing works that make use of accelerometer and gyroscope sensor data for recognizing human activities. This paper presents a comparative study of ensemble learning methods for human activity recognition. The methods include random forest, adaptive boosting, gradient boosting, extreme gradient boosting, and light gradient boosting machine (LightGBM). Among the ensemble learning methods in comparison, light gradient boosting machine and random forest demonstrate the best performance. The experimental results revealed that light gradient boosting machine yields the highest accuracy of 94.50% on UCI-HAR dataset and 100% on single accelerometer dataset while random forest records the highest accuracy of 93.41% on motion sense dataset.
Offline Character Recognition Using Monte Carlo Method and Neural Networkijaia
Human Machine interface are constantly gaining improvements because of increasing development of
computer tools. Handwritten Character Recognition do have various significant applications like form
scanning, verification, validation, or checks reading. Because of the importance of these applications
passionate research in the field of Off-Line handwritten character recognition is going on. The challenge in
recognising the handwritings lies in the nature of humans, having unique styles in terms of font, contours,
etc. This paper presents a novice approach to identify the offline characters; we call it as character divider
approach which can be used after pre-processing stage. We devise an innovative approach for feature
extraction known as vector contour. We also discuss the pros and cons including limitations, of our
approach
HIGHLY SCALABLE, PARALLEL AND DISTRIBUTED ADABOOST ALGORITHM USING LIGHT WEIG...ijdpsjournal
AdaBoost is an important algorithm in machine learning and is being widely used in object detection.
AdaBoost works by iteratively selecting the best amongst weak classifiers, and then combines several weak
classifiers to obtain a strong classifier. Even though AdaBoost has proven to be very effective, its learning
execution time can be quite large depending upon the application e.g., in face detection, the learning time
can be several days. Due to its increasing use in computer vision applications, the learning time needs to
be drastically reduced so that an adaptive near real time object detection system can be incorporated. In
this paper, we develop a hybrid parallel and distributed AdaBoost algorithm that exploits the multiple
cores in a CPU via light weight threads, and also uses multiple machines via a web service software
architecture to achieve high scalability. We present a novel hierarchical web services based distributed
architecture and achieve nearly linear speedup up to the number of processors available to us. In
comparison with the previously published work, which used a single level master-slave parallel and
distributed implementation [1] and only achieved a speedup of 2.66 on four nodes, we achieve a speedup of
95.1 on 31 workstations each having a quad-core processor, resulting in a learning time of only 4.8
seconds per feature.
A Novel Framework For Numerical Character Recognition With Zoning Distance Fe...IJERD Editor
Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach a digit image is divided into 8x8 zones and centre pixel is computed for each zone. This procedure is sequentially repeated for entire zone. Finally features are extracted for classification and recognition.
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ON OPTIMALITY OF THE INDEX OF SUM, PRODUCT, MAXIMUM, AND MINIMUM OF FINITE BA...UniversitasGadjahMada
Chaatit, Mascioni, and Rosenthal de ned nite Baire index for a bounded real-valued function f on a separable metric space, denoted by i(f), and proved that for any bounded functions f and g of nite Baire index, i(h) i(f) + i(g), where h is any of the functions f + g, fg, f ˅g, f ^ g. In this paper, we prove that the result is optimal in the following sense : for each n; k < ω, there exist functions f; g such that i(f) = n, i(g) = k, and i(h) = i(f) + i(g).
Toward a framework for an undergraduate academic tourism curriculum in Indone...UniversitasGadjahMada
We analyse policy documents as well opinions of stakeholders contributing to the development of the undergraduate academic tourism curriculum, namely: The Government which develops the general framework for curriculum development in Indonesian universities; non-governmental tourism associations which assist universities with opinions and guidance; tourism academics who develop and implement the curriculum in the classroom; and tourism trade associations. Two issues characterize the development of the tourism curriculum namely: determining the appropriate balance between vocational and academic frameworks, and an aspiration to move from inter- to mono-disciplinary instruction.
Association of the HLA-B alleles with carbamazepine-induced Stevens–Johnson s...UniversitasGadjahMada
Carbamazepine (CBZ) is a common cause of life-threatening cutaneous adverse drug reactions such as Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). Previous studies have reported a strong association between the HLA genotype and CBZ-induced SJS/TEN.We investigated the association between the HLA genotype and CBZ-induced SJS/TEN in Javanese and Sundanese patients in Indonesia. Nine unrelated patients with CBZ-induced SJS/TEN and 236 healthy Javanese and Sundanese controls were genotyped for HLA-B and their allele frequencies were compared. The HLA-B*15:02 allele was found in 66.7% of the patients with CBZ-induced SJS/TEN, but only in 29.4% of tolerant control (p = 0.029; odds ratio [OR]: 6.5; 95% CI: 1.2–33.57) and 22.9% of healthy controls (p = 0.0021; OR: 6.78; 95% CI: 1.96– 23.38). These findings support the involvement of HLA-B*15:02 in CBZ-induced SJS/TEN reported in other Asian populations. Interestingly, we also observed the presence of the HLA-B*15:21 allele. HLA-B*15:02 and HLA-B*15:21 are members of the HLA-B75 serotype, for which a greater frequency was observed in CBZ-induced SJS/TEN (vs tolerant control [p = 0.0078; OR: 12; 95% CI: 1.90–75.72] and vs normal control [p = 0.0018; OR: 8.56; 95% CI: 1.83–40]). Our findings suggest that screening for the HLA-B75 serotype can predict the risk of CBZ-induced SJS/TEN more accurately than screening for a specific allele.
Characteristics of glucomannan isolated from fresh tuber of Porang (Amorphoph...UniversitasGadjahMada
Porang is a potential source of glucomannan. This research objective was to find a direct glucomannan isolation method from fresh porang corm to produce high purity glucomannan. Two isolation methods were performed. In first method, sample was water dissolved using Al2(SO4)3 as flocculant for 15 (AA15) or 30 (AA30) minutes with purification. In second method, sample was repeatedly milled using ethanol as solvent and filtered for 5 (EtOH5) or 7 (EtOH7) times without purification. The characteristics of obtained glucomannan were compared to those of commercial porang flour (CPF) and purified konjac glucomannan (PKG). High purity (90.98%), viscosity (27,940 cps) and transparency (57.74 %) of amorphous glucomannan were isolated by EtOH7. Ash and protein level significantly reduced to 0.57% and 0.31%, respectively, with no starch content. Water holding capacity (WHC) of EtOH7 glucomannan significantly enhanced, whereas its solubility was lower than those of PKG due to its ungrounded native granular form.
Phylogenetic Analysis of Newcastle Disease Virus from Indonesian Isolates Bas...UniversitasGadjahMada
This study was conducted to analyze phylogenetic of Indonesian newcastle disease virus(NDV) isolates based on fusion (F) protein-encoding gene, with aim to determine which genotype group of Indonesian NDV isolates, compared to vaccine strain that circulating in Indonesia.
Land Capability for Cattle-Farming in the Merapi Volcanic Slope of Sleman Reg...UniversitasGadjahMada
This research carried out to study the cattle farming development based on the land capability in rural areas of the Merapi Volcanic slope of Sleman Regency Yogyakarta after eruption 2010. Samples taken were Glagaharjo village (Cangkringan Sub-District) as impacted area and Wonokerto village (Turi Sub-District) as unimpacted area. Survey method used were to land evaluation analysis supported by Geographic Information System (GIS) software. Materials used were Indonesian topographical basemap (RBI) in 1:25000 scale, IKONOS image [2015], land use map, landform map, and slope map as supple- ments. Potential analysis of land capability for cattle forage using the production unit in kg of TDN per AU. The result showed that based on the land capability class map, both villages had potential of carrying capacity for forage feed that could still be increased as much as 1,661.32 AU in Glagaharjo and 1,948.13 AU in Wonokerto.
When anti-corruption norms lead to undesirable results: learning from the Ind...UniversitasGadjahMada
This paper analyzes how and why adverse side-effects have occurred in the implementation of two articles of Indonesia’s anti-corruption law. These articles prohibit unlawful acts which may be detrimental to the finances of the state. Indeed, the lawmakers had good intentions when they drafted the two articles. They wanted to make it easier to convict corrupt individuals by lowering the standard of evidence required to prove criminal liability. The implementation of these articles has raised legal uncertainty. The loose definition of the elements of the crime enables negligence and imperfection of (public) contracts to be considered as corruption. The Constitutional Court has issued two rulings to restrict and guide the interpretation of these articles. However, law enforcement agencies (Supreme Court and public prosecutors) have been unwilling to adhere to the rulings. There are two possible reasons for this. First, as has been argued by several commentators, the law enforcement agencies have misinterpreted the concept of Bunlawfulness^. Besides, the law enforcement agencies wish to be seen to be committed to prosecuting and delivering convictions in corruption cases. To do so, they need to maintain looser definitions of the elements of the offence. This paper endorses the Constitutional Court rulings and provides additional reasons in support of their stance. The paper can be considered as a case study for other countries that may be contemplating similar legislation.
Receptor binding and antigenic site analysis of hemagglutinin gene fragments ...UniversitasGadjahMada
We reported a retrospective study on hemagglutinin (HA) gene fragments of Avian Influenza (AI) viruses recovered between 2010 to 2012, using reverse transcriptase polymerase chain reaction (RT-PCR) followed by sequencing. The results provide information about the receptor binding sites (RBS) and antigenic sites character of HA gene of AI viruses in Indonesia. Viral RNA was extracted from allantoic fluid of specific pathogen free (SPF) of chicken embryonated eggs inoculated by AI suspected samples. Amplification was performed by using H5 specific primers to produce amplification target of 544 bp. The resulting sequences were analyzed with MEGA-5 consisting of multiple alignment, deductive amino acid prediction, and phylogenetic tree analysis. The results showed that out of the 12 samples amplified using RT-PCR technique, only 7 were detected to be avian influenza serotype H5 viruses. Sequence analysis of AIV H5 positive samples, showed a binding preference towards avian type receptors. Antigenic site analysis is consistent with the previous report, however, the antigenic site B at position 189 showed that the residue had undergone mutation from arginine to methionine. Phylogenetic tree analysis showed that these viruses were clustered into clade 2.1.3. Our report supports the importance of the previous study of RBS and antigenic properties of HPAI H5N1 in Indonesia.
Sustaining the unsustainable? Environmental impact assessment and overdevelop...UniversitasGadjahMada
Bali faces serious environmental crises arising from overdevelopment of the tourism and real estate industry, including water shortage, rapid conversion of agricultural land, pollution, and economic and cultural displacement. This article traces continuities and discontinuities in the role of Indonesian environmental impact assessment (EIA) during and since the authoritarian ‘New Order’ period. Following the fall of the Suharto regime in 1998, the ‘Reform Era’ brought dramatic changes, democratizing and decentralizing Indonesia’s governing institutions. Focusing on case studies of resort development projects in Bali from the 1990s to the present, this study examines the ongoing capture of legal processes by vested interests at the expense of prospects for sustainable development. Two particularly controversial projects in Benoa Bay, proposed in the different historical and structural settings of the two eras—the Bali Turtle Island Development (BTID) at Serangan Island in the Suharto era and the Tirta Wahana Bali Internasional (TWBI) proposal for the other side of Benoa in the ‘Reform Era’—enable instructive comparison. The study finds that despite significant changes in the environmental law regime, the EIA process still finds itself a tool of powerful interests in the efforts of political and economic elites to maintain control of decision-making and to displace popular opposition forces to the margins.
Magnetogama is an open schematic handassembled fluxgate magnetometer. Compared to another magnetometer, Magnetogama has more benefit concerning its price and its ease of use. Practically Magnetogama can be utilized either in land or attached to an unmanned aerial vehicle (UAV). Magnetogama was designed to give open access to a cheap and accurate alternative to magnetometer sensor. Therefore it can be used as a standard design which is directly applicable to the low-budget company or education purposes. Schematic, code and several verification tests were presented in this article ensuring its reproducibility. Magnetogama has been tested with two kind of tests: a comparison with two nearest observatories at Learmonth (LRM) and Kakadu (KDU) and the response of magnetic substance.
Limitations in the screening of potentially anti-cryptosporidial agents using...UniversitasGadjahMada
The emergence of cryptosporidiosis, a zoonotic disease of the gastrointestinal and respiratory tract caused by Cryptosporidium Tyzzer, 1907, triggered numerous screening studies of various compounds for potential anti-cryptosporidial activity, the majority of which proved ineffective. Extracts of Indonesian plants, Piper betle and Diospyros sumatrana, were tested for potential anticryptosporidial activity using Mastomys coucha (Smith), experimentally inoculated with Cryptosporidium proliferans Kváč, Havrdová, Hlásková, Daňková, Kanděra, Ježková, Vítovec, Sak, Ortega, Xiao, Modrý, Chelladurai, Prantlová et McEvoy, 2016. None of the plant extracts tested showed significant activity against cryptosporidia; however, the results indicate that the following issues should be addressed in similar experimental studies. The monitoring of oocyst shedding during the entire experimental trial, supplemented with histological examination of affected gastric tissue at the time of treatment termination, revealed that similar studies are generally unreliable if evaluations of drug efficacy are based exclusively on oocyst shedding. Moreover, the reduction of oocyst shedding did not guarantee the eradication of cryptosporidia in treated individuals. For treatment trials performed on experimentally inoculated laboratory rodents, only animals in the advanced phase of cryptosporidiosis should be used for the correct interpretation of pathological alterations observed in affected tissue. All the solvents used (methanol, methanol-tetrahydrofuran and dimethylsulfoxid) were shown to be suitable for these studies, i.e. they did not exhibit negative effects on the subjects. The halofuginone lactate, routinely administered in intestinal cryptosporidiosis in calves, was shown to be ineffective against gastric cryptosporidiosis in mice caused by C. proliferans. In contrast, the control application of extract Arabidopsis thaliana, from which we had expected a neutral effect, turned out to have some positive impact on affected gastric tissue.
Self-nanoemulsifying drug delivery system (SNEDDS) of Amomum compactum essent...UniversitasGadjahMada
The main purpose of this study was to formulate and to characterize a self-nanoemulsifying drug delivery systems of cardamom (Amomum compactum) essential oil. The optimum formula was analyzed using a D-Optimal mixture designed by varying concentrations of oil component (Amomum compactum essential oil and virgin coconut oil), Tween 80, and polyethylene glycol 400 (PEG 400) (v/v) using a Design Expert® Ver. 7.1.5. Emulsification time and transmittance were selected as responses for optimization. The optimum formula was characterized by droplet size, zeta potential, viscosity, thermodynamic stability, and morphology using Transmission Electron Microscopy. SNEDDS of Amomum compactum essential oil was successfully formulated to SNEDDS using 10% of Amomum compactum essential oil, 10% of virgin coconut oil, 65.71% of Tween 80, and 14.29% of PEG 400. The characterization result showed the percent transmittance 99.37 ± 0.06, emulsification time 46.38 ± 0.61 s, the average droplet size 13.97 ± 0.31 nm with PI 0.06 ± 0.05, zeta potential −28.8 to −45.9 mV, viscosity 187.5 ± 0 mPa·s, passed the thermodynamic stress tests, and indicated spherical shape. The study revealed that the formulation has increased solubility and stability of Amomum compactum essential oil.
Attenuation of Pseudomonas aeruginosa Virulence by Some Indonesian Medicinal ...UniversitasGadjahMada
This study aims to discover quorum sensing inhibitors (QSI) from some Indonesian medicinal plants ethanol extract to analyze their inhibitory activities against QS-mediated virulence factors in P. aeruginosa using in-vitro experimental study-laboratory setting. Indonesian medicinal plant ethanolic extracts were tested for their capability to inhibit P. aeruginosa motility, biofilm formation using microtiter plate method, pyocyanin and LasA production using LasA staphylolytic assay. Statistical significance of the data were determined using one way ANOVA, followed by Dunnett’s test. Differences were considered significant with P values of 0.05 or less. The findings obtained showed that Ethanolic extract of T. catappa leaves and A. alitilis flower capable to inhibit P. aeruginosa motility as well as pyocyanin production and biofilm formation. Both extracts also showed capability in reducing LasA protease production. It is concluded that T. catappa and A. alitilis are an interesting sources of innovative plant derived quorum quenching compound(s), thus can be used in the development of new antipathogenic drug.
Short-chain alcohols are a group of volatile organic compounds (VOCs) that are often found in workplaces and laboratories, as well as medical, pharmaceutical, and food industries. Realtime monitoring of alcohol vapors is essential because exposure to alcohol vapors with concentrations of 0.15–0.30 mg·L−1 may be harmful to human health. This study aims to improve the detection capabilities of quartz crystal microbalance (QCM)-based sensors for the analysis of alcohol vapors. The active layer of chitosan was immobilized onto the QCM substrate through a selfassembled monolayer of L-cysteine using glutaraldehyde as a cross-linking agent. Before alcohol analysis, the QCM sensing chip was exposed to humidity because water vapor significantly interferes with QCM gas sensing. The prepared QCM sensor chip was tested for the detection of four different alcohols: n-propanol, ethanol, isoamyl alcohol, and n-amyl alcohol. For comparison, a non-alcohol of acetone was also tested. The prepared QCM sensing chip is selective to alcohols because of hydrogen bond formation between the hydroxyl groups of chitosan and the analyte. The highest response was achieved when the QCM sensing chip was exposed to n-amyl alcohol vapor, with a sensitivity of about 4.4 Hz·mg−1·L. Generally, the sensitivity of the QCM sensing chip is dependent on the molecular weight of alcohol. Moreover, the developed QCM sensing chips are stable after 10 days of repeated measurements, with a rapid response time of only 26 s. The QCM sensing chip provides an alternative method to established analytical methods such as gas chromatography for the detection of short-chain alcohol vapors.
APPLICATION OF CLONAL SELECTION IMMUNE SYSTEM METHOD FOR OPTIMIZATION OF DIST...UniversitasGadjahMada
This paper proposes an application of clonal selection immune system method for optimization of distribution network. The distribution network with high-performance is a network that has a low power loss, better voltage profile, and loading balance among feeders. The task for improving the performance of the distribution network is optimization of network configuration. The optimization has become a necessary study with the presence of DG in entire networks. In this work, optimization of network configuration is based on an AIS algorithm. The methodology has been tested in a model of 33 bus IEEE radial distribution networks with and without DG integration. The results have been showed that the optimal configuration of the distribution network is able to reduce power loss and to improve the voltage profile of the distribution network significantly.
Screening of resistant Indonesian black rice cultivars against bacterial leaf...UniversitasGadjahMada
Black rice production in Indonesia constrained by the bacterial blight disease (BLB) caused by Xanthomonas oryzae pv. oryzae pathotype IV (Xoo). Breeding of BLB resistant cultivars is considered the most sustainable method for BLB disease control, both from an environmental and agricultural perspective. Indonesia has many local black rice varieties that can be used as genes resource to support breeding program producing resistant cultivars. The present research focuses on screening local Indonesian black rice cultivars for resistance against BLB and analyzing the expression of these resistance genes in black rice after inoculation with Xoo. The black rice cultivars Cempo Ireng, Pari Ireng, Melik, Pendek, and Indmira, were inoculated with Xoo while white rice cv. Conde, IRBB21, IR64, and Java14 were used as controls. We assayed the phenotypic performance of the cultivars samples after Xoo inoculation and analyzed their resistance gene expression at 24 and 96 h after Xoo inoculation semiquantitatively. The cultivar showed the best performance was selected for further analysis of the resistance genes using Realtime quantitative PCR. Cempo Ireng was indicated the most resistant cultivar against BLB disease based on the lowest disease intensity and Area Under Disease Progress Curve (AUDPC) value. Cempo Ireng expressed resistant genes xa5, Xa10, Xa21 and RPP13-like after inoculation of Xoo. The expression of xa5, Xa10, and Xa21 was up-regulated while that of RPP13-like was down-regulated in Cempo Ireng.
This article analyzes the life of young millennial Salafi-niqabi in Surakarta and their strategies in dealing with power relations in their everyday lives. Studies on Salafi in Indonesia have focused more on global Salafimovements, power politics, links with fundamentalist-radical movements, state security and criticism of Salafi religious doctrine. Although there are several studies that try to portray the daily life of this religious group, the majority of previous studies focused on formal institutions and male Salafi. Very few studies have addressed the lives of Salafi women. This is likely due to the difficulty of approaching this group because of their exclusivity, and their restrictions on interacting with the outside world. Using Macleod’s theory of ‘accommodating protest’ within the framework of everyday politics, agency, and power relations, this research found that young millennial Salafi-niqabi have a unique method of negotiating with the modern and globalized world. Through what Macleod calls an accommodation which is at the same time a protest, young Salafi-niqabi have experienced hijrah as a form of negotiation of their millennial identity.
Application of arbuscular mycorrhizal fungi accelerates the growth of shoot r...UniversitasGadjahMada
Shoot roots are second type of root, which emerge from the base of the new shoots, 5-7 days after planting. The shoot roots growth on single bud chips seedling is critical for further growth in dry land. The objectives of this study were to examine shoot root growth using different doses of arbuscular mycorrhizal fungi (AMF) inoculum on five clones of sugarcane and to ascertain their effect on seedling biomass weight. The highest and lowest temperatures on the research site were 32º and 18 ºC, in tropical monsoon climate. The experimental design was a completely randomized design (CRD) in 4x5 factorial arrangement with four replicates. The treatments were: four doses of AMF inoculum (0, 1, 2, 3 g/bud chips) on five clones with single bud chips seedling (PS864, KK, PS881, BL, and VMC). The evaluated parameters were root colonization affected by doses of AMF inoculum, number of shoot roots, surface area of shoot and total roots, root length, biomass seedling, and P leaf concentration affected by doses of AMF inoculum. AMF inoculum doses of 2 and 3 g of inoculum/bud chips resulted in the speed and extent root colonization at 5 days after inoculation on all five sugarcane clones. The clones exhibited 57-100 % accelerated emergence of shoot roots (i.e. the second roots formed), increased total root length, total root surface area especially on BL, VMC, and P leaf concentration. Application of 2-3 inoculum/bud of AMF inoculum significantly increased shoot roots growth i.e. root length, root surface area, and number of shoot roots.
SHAME AS A CULTURAL INDEX OF ILLNESS AND RECOVERY FROM PSYCHOTIC ILLNESS IN JAVAUniversitasGadjahMada
Most studies of shame have focused on stigma as a form of social response and a socio-psychological consequence of mental illness. This study aims at exploring more complex Javanese meanings of shame in relation to psychotic illness. Six psychotic patients and their family members participated in this research. Ethnographic fieldwork was conducted in Yogyakarta, Indonesia. Thematic analysis of the data showed that participants used shame in three different ways. First, as a cultural index of illness and recovery. Family members identified their member as being ill when they had lost their sense of shame. If a patient exhibited behavior that indicated the reemergence of shame, the family saw this as an indication of recovery. Second, as an indication of relapse. Third, as a barrier toward recovery. In conclusion, shame is used as a cultural index of illness and recovery because it associated with the moral-behavioral control. Shame may also be regarded as a form of consciousness associated with the emergence of insight. Further study with a larger group of sample is needed to explore shame as a ‘socio-cultural marker’ for psychotic illness in Java.
Frequency and Risk-Factors Analysis of Escherichia coli O157:H7 in Bali-CattleUniversitasGadjahMada
Cattle are known as the main reservoir of zoonotic agents verocytotoxin producing Escherichia coli. These bacteria are usually isolated from calves with diarrhea and / or mucus and blood. Tolerance of these agents to the environmental conditions will strengthen of their transmission among livestock. A total of 238 cattle fecal samples from four sub-districts in Badung, Bali were used in this study. Epidemiological data observed include cattle age, sex, cattle rearing system, the source of drinking water, weather, altitude, and type of cage floor, the cleanliness of cage floor, the slope of cage floor, and the level of cattle cleanliness. The study was initiated by culturing of samples onto eosin methylene blue agar, then Gram stained, and tested for indole, methyl-red, voges proskauer, and citrate, Potential E.coli isolates were then cultured onto sorbitol MacConkey agar, and further tested using O157 latex agglutination test and H7 antisera. Molecular identification was performed by analysis of the 16S rRNA gene, and epidemiological data was analyzed using
STATA 12.0 software. The results showed, the prevalence of E. coli O157:H7 in cattle at Badung regency was 6.30% (15/238) covering four sub districts i.e. Petang, Abiansemal, Mengwi, and Kuta which their prevalence was 8.62%(5/58), 10%(6/60), 3.33%(2/60), and 3.33(2/60)%, respectively. The analysis of 16S rRNA gene confirmed of isolates as an E. coli O157:H7 strain with 99% similarities. Furthermore, the risk factors analysis showed that the slope of the cage floor has a highly significant effect (P<0.05) to the distribution of infection. Consequently, implementing this factor must be concerned in order to decrease of infection.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
2. 4808 I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection
classifier. The authors in [8] use a neural network and
the gradient magnitude feature for person detection.
Some researchers also modified and combined
the existing methods to improve the performance of
person detection. The authors in [9] combine Haar
wavelets and Edge Orientation Histogram features
and classified them with AdaBoost classifier. In [10]
the authors use random forest to classify DOT (Dom-
inant Orientation Template) features, binary version
of HOG descriptor. The authors in [11] use AdaBoost
algorithm and cascading methods to segment pedes-
trian candidates and make recognizing with SVM
classifier. In [12] the authors proposed a modified
method for HOG, C-HOGC (Combination of HOG
Channels) to perform faster person detection. In very
recent research, such classical gradient features is
still employed with variant of machine learning other
than SVM, e.g. Adaboost [13, 14]. And lastly, Garcia
et al. [15] takes the combination of different sensors
into their approach.
New person detection methods continue to emerge
until now. This encourages some researchers to
perform performance analysis of various existing
methods to know which method works best for person
detection. For instance, the authors in [16] make com-
parison of SVR (Support Vector Regressor) adapted
to binary classification, SVM, and k-NN (k-Nearest
Neighbor) in person detection. In [17], SVM, k-NN,
and decision tree algorithm are used for overlapping
and non-overlapping person detection. There also
exists very recent approach for pedestrian detection,
i.e. using deep learning (e.g. [18]). The problem is,
deep learning method tends to eat huge amount of
resources, making them less deployable on the small
system. In this case, we now focus on the classical
approach which utilizes features and classifiers as its
main core algorithm.
In many researches, the performance of machine
learning is only observed by its accuracy without con-
sidering its processing speed to be deployed in the
real system. Therefore, this research aims to provide
better comparison of machine learning performance
both in terms of accuracy and processing speed. Our
contribution is crystal clear, making a comprehensive
analysis toward learning algorithm used for pedes-
trian detection.
In this research, basic Linear SVM [19], random
forest [20], ERT (Extremely Randomized Tree) [21],
AdaBoost [22, 23], and K-NN are used to classify
HOG features for person detection. Accuracy and
processing speed of the machine learning classifier
are measured and used to calculate the performance
score. The performance score of the machine learn-
ing classifiers are then compared to find out which
machine learning has the best performance in the per-
son detection. The research also focuses on obtaining
optimal value of machine learning parameters. Some
tests and measurement using dataset and stream-
ing frames are observed. The results of the studies
show the effect of varying the value of machine
learning parameters on the performance and provide
performance comparison of several commonly used
machine learning.
2. Basic theory
2.1. Histogram of Oriented Gradient (HOG)
Histogram of Oriented Gradient [1] is descriptor
that extracts image features from calculation of gra-
dient vector of pixels and distributes them to gradient
histograms. This descriptor introduced by Navneet
Dalal and Bill Triggs has exceled performance espe-
cially for person detection compared by preceding
descriptors based on edge and gradient of image. In
general, this descriptor works by dividing images
window into some small spatial regions, called as
“cell”, for each cell accumulating a local 1-D his-
togram of gradient directions or edge orientations
over the pixels of the cell. The combined histogram
then entries form representation. For better invariance
to illumination, shadowing, etc., contrast- normalized
is done by normalize histogram features for every
larger spatial regions, called as “block cell”.
In this research, the histogram contains 9 bins
corresponding to angles 0, 20, 40, . . . , 160. Each
block cell contains 4 cells that each has 9 values of
histogram as features. Each block cell contain 36 fea-
tures vector that will be normalized. In 48 × 96 pixels
window, there are 3 × 6 block cell (no overlapping)
that each contains 36 features, so the output of the
HOG descriptor in this research is 648 features. The
process of each cell in this descriptor is illustrated by
Fig. 1.
2.2. Machine Learning Algorithm for Pedestrian
Detection
Machine learning is a subfield of computer sci-
ence that gives computer ability to learn without
being explicitly programmed [20]. Machine learn-
ing focus on the development of computer programs
to learn data and then provide processing function
3. I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection 4809
Fig. 1. HOG Descriptor Cell Processing.
to new data. Machine learning algorithms are often
categorized as being supervised or unsupervised.
Supervised machine learning learns by knowing first
the desired input and output data and then creat-
ing some mathematical functions from the data. This
research uses 5 commonly used supervised machine
learning algorithms to classify HOG features for per-
son detection.
2.2.1. Support Vector Machine (SVM)
The original Support Vector Machine algorithm
[19] was invented by Vladimir N. Vapnik and Alexey
Ya. Chervonenkis in 1963. The algorithm is based on
finding hyperplane that gives the largest minimum
distance to the training examples The notation used
to define linear hyperplane is shown in eq. 1, where
variable w, x, and b are weight vector, input vector,
and weight bias respectively.
f(x) = wT
x + b. (1)
By using the result of geometry, the orthogonal
distance between vector x and hyperplane can be
derived equal to |f(x)|
w . If the training examples clos-
est to hyperplane, called as support vectors, are set to
have f(x) equal to 1, the distance between two oppo-
site support vectors orthogonal to the hyperplane,
or called as hyperplane margin, can be calculated
equal to 2
w . The problem of maximizing mar-
gin is equivalent to the problem of minimizing
a function L(w) subject to some constraints. The
constraints model for the hyperplane to classify
all training examples correctly can be formulated
by eq. 2, where variable yi is label of ith train-
ing examples. The problem of this optimization can
be solved using Lagrange multipliers to obtain the
weight vector w and bias b of the optimal hyper-
plane.
min
w,b
L(w) =
1
2
w 2
, s.t. yif(xi) ≥ 1, ∀i. (2)
If SVM algorithm allows some misclassifications
in training, known as soft margin SVM, the con-
straints model of hyperplane can be formulated by eq.
3, where C is regularization parameter and ζi is dis-
tanceerrortothehyperplaneandequalto1 − yif(xi).
The minimum value of the model is also can be
found by Lagrangian multiplier with set value of
C parameter. C parameter will determine the mar-
gin of SVM. SVM with higher C value tends to
make narrower margin. In the process of finding
the hyperplane, the value of C will be multiplied
by training examples distance error ζi. SVM with
lower C value is more indifferent to misclassificar-
tion error and tends to make hyperplane with wider
margin.
min
w,b
L(w) =
1
2
w 2
+ C
N
i=0
ζi, s.t. yif(xi) ≥ 1 − ζi, ∀i.
(3)
4. 4810 I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection
2.2.2. Random forest
Random forest algorithm [20] has been introduced
by Leo Breiman. Random Forest is a collection
(ensemble) of tree predictor or decision tree. In gen-
eral, the random forest algorithm works by taking the
input feature vectors at set random values, classifying
it with every tree. The output of the classifier is the
class label that received the majority of votes from all
trees. In training, random forest uses bootstrap pro-
cedure to avoid over-fitting so all the trees are trained
on different training set. Each tree samples N training
examples randomly from N training examples of the
training set with replacement. From the N training
examples with M input variables, each tree only uses
m input variables selected randomly. The N training
examples with m input variables are then used for
growing the tree to the largest extent possible. Each
node of the tree finds the best split based on gini index
of all possible splits of all features. For example, if
the node a is split into two child nodes, node b and
node c, and let variable nx, nxpos, nxneg be the num-
ber of training examples, number of positive training
examples, and number of negative training examples
in node x respectively, gini index of split Ig in the
node a can be calculated by eq. 4. Higher gini index
of split indicates better split.
I(x) = 1 −
nxpos
nx
−
nxneg
nx
Ig(a) = I(a) −
nb
na
I(b) −
nc
na
I(c)
(4)
The accuracy of random forest is determined by the
two things, i.e. the correlation between any two trees
and the strength of each individual tree. Increasing
the number of random selected features will increase
the correlation between any two trees and the strength
of each individual tree. Increasing the correlation will
increase the random forest error rate, while increasing
individual tree strength will decrease the random for-
est error rate. The optimal number of random selected
features can be obtained by performance test
2.2.3. AdaBoost
AdaBoost or Adaptive Boosting [22] is introduced
in 1995 by Yoav Freund and Robert Schaphire. It is
a boosting technique that combines multiple weak
classifiers into a single strong classifier. AdaBoost
works by assigning weight to each training examples.
After training a classifier, AdaBoost will increase the
weight on the misclassified examples and reduce the
weight on classified example, so the next classifier
will classify them differently. After each classifier is
trained, the classifier weight is calculated based on its
accuracy; accurate classifier are given more weight,
classifier with 50% accuracy is given a weight of zero,
and classifier with less than 50% accuracy is given
negative weight. The final output of AdaBoost, H(x)
is the signum function of linear combination of all
the weak classifier, shown in eq. 5, where variable T,
αt, ht(x), and x are number of weak classifier, weight
applied to tth classifier, output of tth weak classifier,
and vector of new example respectively.
H(x) = sign
T
t=1
αtht(x) (5)
First classifier (t = 1) is trained with equal training
example weight (= 1
m ), for all m training examples.
The weight of the classifier then can be computed by
formula shown in eq. 6, where t is the total weights
of misclassification example over the total weights of
the training set divided by the training set size.
αt =
1
2
ln
1 − t
t
(6)
After computing αt, the training examples weight
are updated using formula shown in eq. 7, where yi
denotes the desired output and Zt is the normalization
constant so the sum of the new weights of training
example is equal to 1.
Dt+1(i) =
Dt(i) exp(−αtyiht(xi))
Zt
(7)
2.2.4. Extremely randomized trees
Extremely randomized trees [21] have been intro-
duced by Pierre Geurts, Damien Ernst, and Louis
Wehenkel in 2006. The algorithm is almost similar
to random forest algorithm. Unlike random forest
algorithm, this algorithm does not apply bootstrap
procedure, so every tree is trained with the same train-
ing set. The other difference is the algorithm pick a
node split randomly. In random forest, gini index of
every possible split in every variable is evaluated. The
split with the highest score is chosen for splitting the
node. In ERT, split value is a random value between
the smallest and the highest value in each variable.
The score of random splits in every variable are then
evaluated based on gini index also. The split with
highest score is chosen for splitting the node.
5. I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection 4811
Fig. 2. Proposed block diagram for pedestrian detection.
2.2.5. k-Nearest Neighbor (k-NN)
k-NNisoneofthesimplestalgorithmsavailablefor
supervised learning. The idea is to search for closest
match of the test data in feature space. The training
examples features are stored for later use to calcu-
late feature space distance to the new example. The
research uses Euclidean distance formula to calcu-
late the feature space distance of new example to
each training examples. The output is the majority
class label of the training examples with k shortest
distance to the new example.
2.3. Sliding Windows for Detection
Slidingwindowisaconventionalmethodforobject
detection that works by moving detection window
horizontally and vertically along the processed frame.
Image in detection window size is cropped from the
processed frame at every window position and pro-
cessed one by one. The detection window must have
the same size with the training images. The HOG fea-
ture set is then extracted from each cropped region
and classified by the machine learning classifier. If
the classifier gives positive result, bounding box at
window position will be displayed on the processed
frame.
To perform multi scale detection, the processed
frame must be resized multiple times, so the detection
window can classify small and large objects. In multi
scale detection, many bounding boxes will appear
overlap each other. NMS (Non-Maxima Suppression)
algorithm must be applied to solve the problem. The
algorithm works by choosing one bounding box that
has highest classifier confidence score from among
the multiple overlapping boxes. The two bounding
boxes will be considered as overlapping box if the
overlapping area is more than half of the area of one
of the overlapping boxes.
3. Methodology
Our work on the pedestrian detection is divided
intotwoparts,trainingandtesting.Theblockdiagram
of this process is shown in Fig. 2. In training part,
person images (Fig. 3a) and non-person images (Fig.
3b) are first resized to 48 × 96 pixels size.
After pre-processing, HOG feature set is extracted
from each image. The feature sets and the ground
truth values of the training images are then used to
train the machine learning approach.
In dataset testing, new images are also resized
to 48 × 96 pixels size. The HOG feature set of the
imagesarethenextracted.Thefeaturesetoftheimage
is classified by the machine learning classifier and
compared to the image ground truth. In video testing,
the frames are resized multiple times. HOG feature
extraction and detection are done while applying slid-
ing window method. The 48 × 96 pixels image are
cropped at every position of detection window and
the HOG feature set are extracted. NMS algorithm
is then applied after sliding window process is com-
plete. The bounding boxes are then displayed on the
processed frame and visually observed.
4. Results and discussions
In this research, system performance is observed
in several detection tests. First, the classifier per-
formance with various parameter values in dataset
testing are observed to get the optimal value of
6. 4812 I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection
Fig. 3. Pedestrian dataset: (a) positive images, and (b) negative images.
parameter. The classifier performance with the opti-
mal value of parameters is then compared in 5-cross
validation. The precision and recall of machine lean-
ings are also observed in video test. The results show
the superiority of SVM performance in person detec-
tion using HOG features compared with the other
algorithms.
4.1. Tools and Materials
Tools used in the research are as follows:
1. Computer with specification Windows 7 oper-
ating system, processor Intel(R) Core(TM) i3
CPU M370 @2.4 GHz memory 2,00 GB.
2. OpenCV Library 2.4.9.
Material in the form of person and non-person
images is obtained from INRIAPerson, MIT, Penn-
sylvania dataset, and some videos of ours. The image
numbers of each dataset will be explained in each test
below.
It is also important to be noted that the provided
computation time for the performance measurements
in all experiments are restricted relatively to the above
system setup. Any other system may yield different
timing.
4.2. Effect of Varying the Value of Parameters on
Machine Learning Classifier Performance
To show the effect of varying the value of machine
learning parameters on the performance, the accu-
racy, processing time, and performance score of
machine learning classifier are measured. Accuracy is
measured by area under ROC curve, time by average
processing time per image, and performance score
by subtraction of min-max normalized accuracy and
speed value. Accuracy are min-max normalized from
[0.9, 1] to [0, 1] and speed value from [0, 0.05] to
[0, 1]. In this test, there are two cases with differ-
ent training and testing data composition; the case A
Table 1
Effect of Varying Parameter on Linear SVM Performance
Case A
C AUC time (s) score
0.01 0.9949 9.89e-07 0.9488
0.1 0.9951 9.73e-07 0.9508
1 0.9928 9.71e-07 0.9278
Case B
C AUC time (s) score
0.01 0.9953 9.84e-07 0.9528
0.1 0.9955 9.71e-07 0.9551
1 0.9915 9.76e-07 0.9148
uses training set containing 8K/16K positive/negative
images and testing set containing 1500/3K posi-
tive/negative images. The case B uses training set
containing8K/32Kpositive/negativeimagesandtest-
ing set containing 1500/6K positive/negative images.
4.2.1. Support Vector Machine
In this test, only the value of parameter regulariza-
tion (C) is varied. The value of C is set to 0.01, 0.1,
and 1. SVM does not use any kernel in this research
because linear SVM has been able to do classifica-
tion well for this case. The result of the test for case A
and case B are displayed on Table 1. The result of the
test shows that SVM gives very fast processing time
and very high accuracy. The time here is the aver-
age of processing time per image in second. SVM
has very simple computation resulting very fast pro-
cessing time. Table 1. shows no major difference of
SVM performance for case A and case B. The result
also shows that SVM with C = 0.1 give the best per-
formance in the accuracy and the performance score.
The C value determines the width of SVM hyper-
plane margin. The higher the C value, the wider the
margin. SVM that has too narrow hyperplane margin
will give many misclassifications in high variance
testing data, whereas too wide margin SVM will
misclassify many training examples as error. The lat-
ter will also give many misclassifications in testing
data if the set contains many examples similar to the
7. I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection 4813
Fig. 4. Effect of Varying The Value of Parameters on Random Forest Performance.
misclassified training examples. The good C value
must be obtained by varying and observing the C
value in some range or one can calculate it adap-
tively. In our case, the chosen C value is 0.1, based
on the observation to its range we have mentioned
in the beginning. Such C value may be different for
different classification case.
4.2.2. Random Forest
In this test, the number of random selected fea-
tures, number of trees, and split depth level of random
forest classifier are varied. The results are shown in
Fig. 4. In the graphs, the blue lines, the red lines,
and the green lines represent random forest with 50,
100, and 200 random selected features respectively.
The dotted lines are test results for case A and the
continuous lines are test results for case B. Figure
4a shows that increasing the number of trees will
increase accuracy to the convergence value. In this
test, the split depth level of the tree is set to maximum
number. The combination of more weak classifiers
tends to make stronger classifier. In a certain num-
ber of trees, the correlation between the trees reach
optimal value, so increasing the number of trees will
no more increase accuracy. In case A and case B,
The result shows that random forest with 100 random
selected features give the highest accuracy (in the
range 0.986 to 0.988). Random forest with too high
number of random selected features will have too cor-
related trees. Many trees will work the same way
in classification, so random forest becomes ineffi-
cient and will have bad performance. Random forest
with too small number of random selected features
will have very weak trees. More number of random
selected features in random forest will give each tree
higher probability to make better split in each node
resulting a stronger tree.
Figure 4b shows the effect of increasing num-
ber of trees to the average processing time per
image. Time scale here is in 10 base logarithmic
scales. The graph shows that increasing the num-
ber of the trees will increase the processing time.
Each tree in random forest will take different time
for classification, so increasing the number of trees
will increase time needed to finish all tree classi-
fications. The result also shows that random forest
with more number of selected features has faster
processing time. Random forest with more random
selected features tends to give tree better split to
separate the two classes completely. The number of
nodes of the tree will be smaller and result faster
classification.
8. 4814 I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection
Figure 4c shows the performance score of random
forest classifier in the effect of varying the number of
trees. The result shows the optimal number of tree is
in the range 80 to 140. Increasing the number of tree
at the certain number will increase accuracy very lit-
tle or no more, but the time keeps increasing in fixed
amounts, so the performance score will decrease. Fig-
ure 4d and 4e shows the effect of split depth level on
the random forest accuracy and processing time. In
this test number of trees is set to 300. Split depth
level here is a limitation of split level of the trees.
The result shows that increasing the split depth level
tends to increase accuracy and processing time to the
convergence value. Higher level node contains more
classified training examples, so the split in that level
become less important and less influential. Decision
tree with high split depth level tends to overfit the
training examples, but it is hard to see the effect on
the random forest performance. Figure 4f shows the
performance score of random forest in the effect of
changing the split depth level. The result shows that
the effective split level of random forest is in the range
10 to 12. This parameter is highly dependent on the
tree structures of random forest so different random
forest may result different optimal value.
4.2.3. Extremely Randomized Tree (ERT)
In this test, the number of random selected fea-
tures, number of trees, and split depth level of ERT
classifier are varied. The results are shown in Fig. 5.
In the graphs, the blue lines, the red lines, and the
green lines represent ERT with 50, 100, and 200 ran-
dom selected features respectively. The dotted lines
are the test results for case A and the continuous lines
are the test results for case B. Figure 5a shows that
increasing number of trees will increase ERT accu-
racy to the convergence value. In this test, the split
depth level of the tree is set to maximum number.
The graph shows almost the same result with Fig-
ure 4a. Increasing number of trees will also make
ERT stronger classifier. The result shows that ERT
with 200 random selected features give the high-
est accuracy up to 0.988. Correlation between the
trees in ERT is lower than correlation between the
trees in random forest. Two trees in ERT with simi-
lar random selected features may still have different
classification pattern because the split value in ERT is
chosen randomly. This makes ERT with high number
of random selected features can still have good accu-
racy. Nevertheless, increasing the number of random
selected features too much will also make ERT trees
too correlated resulting bad performance.
Figure 5b shows the effect of increasing number of
trees on the average ERT processing time per image.
Time scale here is in 10 base logarithmic scales. The
graph shows almost the same results with Figure 4b.
Increasing the number of the trees will increase the
processing time. ERT with more number of selected
features have faster processing time. This is the same
case with random forest. ERT also chooses the best
split among the random split of all features, so ERT
with more number of random selected features tends
to make better split in each node and results faster
classification. In ERT, there is a considerable amount
of time difference between case A and case B. ERT
takes longer time than random forest to separate train-
ing examples classes completely. The split quality in
ERT is much lower than random forest split. ERT just
pick random value for every feature and does not eval-
uate all possible splits, so increasing the number of
training examples will increase the processing time
more significantly. Figure 5c shows that ERT with
200 features has the highest performance score. Fig-
ure 5d and 5e shows the effect of split depth level on
the accuracy and processing time of ERT. In this test
number of trees is set to 300. Split depth level here
is a limitation of split level of all trees. The result
shows that increasing the split depth level tends to
increase accuracy and processing time to the conver-
gence value. Figure 5f shows the performance score
of ERT in the effect of changing the split depth level.
The result shows different optimal split level in dif-
ferent case and different parameter value.
4.2.4. AdaBoost
In this test, number of trees and split depth of
AdaBoost classifier are varied. The results are shown
in Fig. 6. In the graphs, the blue lines, the red lines,
and the green lines represent AdaBoost with 2, 5, and
10 split depth levels respectively. The dotted lines are
the test results for case A and the continuous lines are
the test results for case B.
Figure 6a shows that increasing the number of
trees in AdaBoost will also increase accuracy to the
convergence value. This is the same case with RF
and ERT that the more combination of weak classi-
fier will result stronger classifier. The result shows
that AdaBoost with 5 split depth level has the best
accuracy (in the range 0.986 to 0.988). All trees in
AdaBoost except the first, will classify the train-
ing examples that are weighted adaptively based on
previous tree misclassification. The tree classifiers
then are also weighted based on their each perfor-
mance. The more accurate tree will be assigned a
9. I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection 4815
Fig. 5. Effect of Varying the Value of Parameter on Extremely Randomized Tree Performance.
Fig. 6. Effect of Varying the Value of Parameters on AdaBoost Performance.
higher weight. AdaBoost with too low split level
may result too many misclassification error, so the
training examples weights will be changed too fast.
In high variance training examples, AdaBoost with
very low split depth level may have most of the
trees having very low accuracy and assigned very
low weights. This combination of poor trees will
result the bad performance of AdaBoost. In con-
trary, AdaBoost with too high split level will result
too few misclassification error, so the training exam-
ples weights will be changed too slow. This will also
result highly correlated trees and bad performance in
AdaBoost.
Figure 6b shows the effect of increasing the
number of trees on the average AdaBoost processing
time per image. Time scale here is in 10 base
logarithmic scales. The graph shows almost the
same result with random forest and ERT. Increasing
the number of trees will increase computation time.
The result also shows that AdaBoost with more split
depth level take more processing time. This is also
the same case with the random forest and ERT that
10. 4816 I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection
Fig. 7. Effect of Varying the Value of Parameters on k-NN Performance.
increasing the split depth level will increase the
processing time. The processing time of AdaBoost
for case A and case B are the same because the trees
still have the same tree depth split level although
they have different number of training examples.
Figure 6c shows that AdaBoost with 5 split depth
level has the highest performance score.
4.2.5. k-Nearest Neighbor
In the test, number of features used to calcu-
late the distance and number of training examples
in k-NN are varied. The results are shown in Fig.
7. In the graphs, the dark blue lines, the magenta
lines, the green lines, the purple lines, the light
blue lines, and the brown lines represent the k-NN
using only 50, 100, 150, 200, 250, and 300 fea-
tures respectively. In this test k-NN does not use all
HOG features for computing the distance, but only
take some features that have highest gini index of
split. The k value is set to square root of the num-
ber of training examples. Figures 7a and 7d show
the effect of varying the number of training example
on k-NN accuracy for case A and case B respec-
tively. In the test, k-NN training examples are scale
down by the factor of 2 from 1, 1/2, 1/4, . . . , 1/128
of the number of training examples. The results
show that reducing the number of training examples
and will reduce the k-NN accuracy. Reducing the
number of training examples will reduce the vari-
ance of training examples and result less accurate
classification. The results also show that k-NN clas-
sifiers that use 50 and 100 features give the highest
accuracy.
Figure 7b and 7e show the effect of varying the
number of training example on the k-NN processing
speed. The results show that reducing the train-
ing examples will reduce considerable amount of
processing time value. k-NN works by measuring
distance between the new example and all train-
ing examples. k-NN with more number of training
examples will take longer time in classification. The
results also show that k-NN that uses fewer fea-
tures have faster processing speed. Figure 7c and 7f
show that k-NN that uses 50 features has the best
performance for both case A and case B. Reduc-
ing the number of less important feature in k-NN
reduces the processing time significantly resulting
better performance score. The optimal number of
11. I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection 4817
Table 2
Cross Validation Test
Fold Measured Machine Learning Classifier
SVM RF ERT ADA k-NN
k=1 AUC 0.9880 0.9783 0.9789 0,9897 0,9851
time (s) 1.20e-06 9.10e-05 9.00e-05 1.60e-05 1.50e-04
score 0.8796 0.7646 0.7705 0.8805 0.8218
k=2 AUC 0.9927 0.9849 0.9858 0.9945 0.9818
time (s) 1.00e-06 6.70e-05 7.70e-05 1.30e-05 3.80e-04
score 0.9262 0.8361 0.8425 0.9322 0.7431
k=3 AUC 0.9965 0.9876 0.9893 0.9969 0.9771
time (s) 1.00e-06 4.90e-05 7.10e-05 9.20e-06 3.80e-04
score 0.9643 0.8658 0.8784 0.9602 0.6964
k=4 AUC 0.9964 0.9893 0.9915 0.9969 0.9799
time (s) 9.80e-07 6.10e-05 9.20e-05 1.00e-05 3.80e-04
score 0.963 0.8812 0.8961 0.9591 0.7232
k=5 AUC 0.9955 0.9864 0.9889 0.9964 0.9776
time (s) 1.00e-06 6.00e-05 9.30e-05 1.30e-05 3.80e-04
score 0.9542 0.8518 0.8707 0.9506 0.7005
Mean AUC 0.9938 0.9853 0.9869 0.9949 0.9803
time (s) 1.00e-06 6.60e-05 8.50e-05 1.20e-05 3.30e-04
score 0.9375 0.8399 0.8516 0.9365 0.737
training examples here is 1/64 of the original number
of training examples. Reducing this parameter will
reduce the accuracy slightly, but reduce the process-
ing time significantly.
4.3. Performance Comparison of Machine
Learning in Dataset Test
Performance of machine learning in this sec-
tion is measured in 5-cross validation test using
8K/80K positive/negative training examples. In this
test, parameter of machine learning classifiers is set
to the optimal value obtained in previous test. The C
parameter of SVM is set to 0.1. Random forest is set
to have 100 random selected features. ERT is set to
have 200 random selected features. AdaBoost is set
to have 5 level of split depth. k-NN is set to only use
50 features for measuring the distance. Other param-
eters are varied and selected the best. The result of
the test is shown in Table 2.
The result shows that linear SVM has the best
performance among the other classifiers. SVM has
advantages in high-dimensional data classification.
HOG is very effective feature set for person detec-
tion. It gives almost linear separable features that
SVM can classify the two classes very well (up to
98% accuracy in training examples). SVM also has
very fast processing time The second classifier that
has the best performance is AdaBoost. AdaBoost has
accuracy as high as SVM accuracy, but with higher
processing time. AdaBoost consists of many adaptive
learning trees combined into very strong classifier.
Fig. 8. Performance of machine learning classifications on video
test.
The third and the forth are ERT and random forest.
ERT has higher accuracy but also higher processing
time than random forest. ERT trees have lower cor-
relation than random forest trees, so the combination
of trees is better and gives higher accuracy. Random
forest has better node split than ERT resulting faster
classification time. Random forest and ERT may have
superior performance if they are used for classifi-
cation of very large low-dimensional data. Random
forest and ERT also can be used for more than two
classes classification that single SVM and AdaBoost
cannot. k-NN has the worst performance among the
classifiers in this case. k-NN is very slow for pro-
cessing the large data. Reducing the training data in
k-NN will reduce the processing time but also reduce
the accuracy. k-NN may be suitable for multi-class
12. 4818 I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection
Fig. 9. Pedestrian detection performance on frame test.
classification that does not necessarily require fast
processing.
4.4. Performance Comparison of Machine
Learning in Video Test
In this test, machine learning classifiers are used
to perform person detection on video containing high
variance and very unbalanced-class data. The video
consists of 260 frames in size of 640x480 pixels.
The frames are resized into 7 scales to make multi-
scale detection. The machine learnings in this test
are trained by 9500/75K positive/negative training
examples. In the test, C parameter of SVM is set
to to 0.1. Random forest is set to have 100 ran-
dom selected features, 150 trees, and maximum split
depth level. ERT is set to have 200 random selected
features, 150 trees and maximum split depth level.
AdaBoost is set to have 5 level of split depth and
300 trees. k-NN is set to use 50 features and 1/128
of the number of training example. The performance
of machine learning classifier is plot in the precision-
13. I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection 4819
recall graph shown in Fig. 8. The graph shows that
SVM and AdaBoost have the highest accuracy in this
test. SVM separates the features of the two classes
with quite big margin resulting robust performance
in various data condition. The processing speed of
SVM in this test varies from 10.49 FPS to 11.11
FPS.
The performance of tree-based classifier usually
decreases when dealing with very unbalanced-class
data. Tree classifier tends to overfit the training data.
Training data with more unbalanced class will make
tree classifier better in classifying the major class
but worse in classifying the minor class. Although
AdaBoost is combination of many tree classifiers,
the performance is not significantly affected by the
classunbalance.AdaBoosttreesaregrownwithadap-
tively weighted data and limited split level, so they
rarely overfit the data. AdaBoost classifier with 300
trees in this test runs at 8.44 FPS to 8.90 FPS. ERT
and random forest performance also does not change
significantly in this test. The classifiers in this test
are trained with training data that have very unbal-
anced class as well so their performance does not
significantly change dealing with the condition. ERT
runs at 3.56 to 4.15 FPS and random forest runs
faster at 5.28 to 5.82 FPS. k-NN has the lowest
accuracy in this test. k-NN in this test use only 50
features and 1/128 of the number of training exam-
ples to support its speed. This make the training data
become less variance resulting low accuracy perfor-
mance. Even with these limitations, k-NN still runs
slow at 3.23 to 3.70 FPS. Classification result of
some machine learning in the frame test is shown in
Fig. 9.
5. Conclusions
Among the compared machine learning algo-
rithms, SVM has the best performance on person
detection using HOG features. HOG feature set gives
almost linear separable features that linear SVM can
work best. The optimal value of machine learning
parameters can be obtained by varying the value of
parameters and observing the performance in dataset
test. In detection with high-spec computer, extracting
HOG features with larger window size, i.e. 64 × 128
pixels and overlapping block cells may increase the
overall performance of the classifiers. Larger and
more variance training data may be required for better
detection. Tracking algorithm may also be required
to make the detection more stable.
References
[1] N. Dalal and B. Triggs, Histograms of oriented gradi-
ents for human detection, in 2005 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition
(CVPR’05), vol. 1, 2005, pp. 886–893.
[2] M. Oren, C. Papageorgiou, P. Sinha, E. Osuna and T. Poggio,
Pedestrian detection using wavelet templates, in Proceed-
ings of IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, 1997, pp. 193–199.
[3] C. Papageorgiou and T. Poggio, Trainable pedestrian detec-
tion, in Proceedings 1999 International Conference on
Image Processing (Cat. 99CH36348), vol. 4, 1999, pp.
35–39.
[4] P. Viola, M.J. Jones and D. Snow, Detecting pedestrians
using patterns of motion and appearance, in Proceedings
Ninth IEEE International Conference on Computer Vision,
vol. 2, 2003, pp. 734–741.
[5] A. Mohan, C. Papageorgiou and T. Poggio, Example based
object detection in images by components, IEEE Trans Pat-
tern Anal and Machine Intell 23 (2001), 349–361.
[6] G. Grubb, A. Zelinsky, L. Nilsson and M. Rilbe, 3d vision
sensing for improved pedestrian safety, in IEEE Intelligent
Vehicles Symposium, 2004, pp. 19–24.
[7] A. Shashua, Y. Gdalyahu and G. Hayun, Pedestrian
detection for driving assistance systems: Single-frame clas-
sification and system level performance, in IEEE Intelligent
Vehicles Symposium, 2004, pp. 1–6.
[8] L. Zhao and C.E. Thorpe, Stereo- and neural network-based
pedestrian detection, IEEE Transactions on Intelligent
Transportation Systems, vol. 1, 2000, pp. 148–154.
[9] D. Geronimo, A.D. Shappa, A. Lopez and D. Ponsa, Pedes-
trian detection using adaboost leaning of features and
vehicle pitch estimation, in The Sixth IASTED Interntional
Conference, Visualization, Imaging, and Image Processing,
2006, pp. 400–405.
[10] D. Tang, Y. Liu and T. Kim, Fast pedestrian detection by cas-
caded random forest with dominant orientation templates,
in Proceeding of the British Machine Vision Conference
(BMCV2012), 2012, pp. 1–11.
[11] R.A. Kharjul, V.K. Tungar, Y.P. Kulkarni, S.K. Upadhyay
and R. Shirsath, Real-time pedestrian detection using svm
and adaboost, in 2015 International Conference on Energy
Systems and Applications, 2015, pp. 740–743.
[12] L. Weixing, S. Haijun, P. Feng, G. Qi and Q. Bin, A fast
pedestrian detection via modified hog feature, in 2015 34th
Chinese Control Conference (CCC), 2015, pp. 3870–3873.
[13] L. Guo, P.-S. Ge, M.-H. Zhang, L.-H. Li and Y.-B. Zhao,
Pedestrian detection for intelligent transportation systems
combining adaboost algorithm and support vector machine,
Expert Systems with Applications 39(4) (2012), 4274 –4286.
[14] V.-D. Hoang, M.-H. Le and K.-H. Jo, Hybrid cascade
boosting machine using variant scale blocks based hog fea-
tures for pedestrian detection, Neurocomputing 135 (2014),
357 –366.
[15] F. Garcia, J. Garcia, A. Ponz, A. de la Escalera and
J.M. Armingol, Context aided pedestrian detection for
danger estimation based on laser scanner and computer
vision, Expert Systems with Applications 41(15) (2014),
6646–6661.
[16] M. Errami and M. Rziza, Improving pedestrian detection
using support vector regression, in 2016 13th International
Conference on Computer Graphics, Imaging and Visualiza-
tion (CGiV), 2016, pp. 156–160.
14. 4820 I. Ardiyanto et al. / On comprehensive analysis of learning algorithms on pedestrian detection
[17] B. Amirgaliyev, K. Perizat and C. Kenshimov, Pedestrian
detection algorithm for overlapping and non-overlapping
conditions, in 2015 Twelve International Conference on
Electronics Computer and Computation (ICECCO), 2015,
pp. 1–4.
[18] S. Ren, K. He, R. Girshick and J. Sun, Faster r-cnn: Towards
real-time object detection with region proposal networks,
in Advances in Neural Information Processing Systems 28
(C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R.
Garnett, eds.), Curran Associates, Inc., 2015, pp. 91–99.
[19] V.N. Vapnik, Statistical Learning Theory. Wiley-Inter-
science, 1998.
[20] L. Breiman, Random forests, Machine Learning 45 (2001),
5–32.
[21] P. Geurts, D. Ernst and L. Wehenkel, Extremely randomized
trees, Machine Learning 63 (2006), 3–42.
[22] Y. Freund and R.E. Schapire, A decision-theoretic general-
ization of on-line learning and an application to boosting,
Journal of Computer and System Sciences 55(1) (1997),
119–139.
[23] R.E. Schapire, The Boosting Approach to Machine Learn-
ing: An Overview, New York, NY: Springer New York,
2003, pp. 149–171.