Human activity recognition is an important task in computer vision because it has many application areas
such as, healthcare, security, entertainment, and tactical scenarios. This paper presents a methodology to
automatically recognize human activity from input video stream using Histogram of Oriented Gradient
Pattern History (HOGPH) features and SVM classifier. For this purpose, the proposed system extracts
HOG features from a sequence of consecutive video frames and analyzes them to construct HOGPH feature
vector. The HOGPH feature vectors are used to train a multi-class SVM classifier for different human
activities. In test mode, we use the classifier with HOGPH feature vector to recognize human activity. We
have experimented with video data of human activity in real environments for three different tasks
(browsing, reading, and writing). The experimental result and its accuracy reveal that the proposed system
is applicable to recognize human activity in real-life.
Human activity recognition is an important task in computer vision because it has many application areas
such as, healthcare, security, entertainment, and tactical scenarios. This paper presents a methodology to
automatically recognize human activity from input video stream using Histogram of Oriented Gradient
Pattern History (HOGPH) features and SVM classifier. For this purpose, the proposed system extracts
HOG features from a sequence of consecutive video frames and analyzes them to construct HOGPH feature
vector. The HOGPH feature vectors are used to train a multi-class SVM classifier for different human
activities. In test mode, we use the classifier with HOGPH feature vector to recognize human activity. We
have experimented with video data of human activity in real environments for three different tasks
(browsing, reading, and writing). The experimental result and its accuracy reveal that the proposed system
is applicable to recognize human activity in real-life.
An Efficient Activity Detection System based on Skeleton Joints Identification IJECEIAES
The increasing criminal activities in the current world has drawn lot of interest activity recognition techniques which helps to perform the sophistical analytical operations on human activity and also helps to interface the human and computer interactions. From the existing review analysis it is found that most of the existing systems are not emphasize on computational performance but are more application specific by identifying specific problems. Hence, it is found that all the features are not required for accurate and cost effective human activity detection. Thus, the human skelton action can be considered and presented a simple and accurate process to identify the significant joints only. From the outcomes it is found that the proposed system is cost effective and computational efficient activity recognition technique for human actions.
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.
Human activity recognition is an important task in computer vision because it has many application areas
such as, healthcare, security, entertainment, and tactical scenarios. This paper presents a methodology to
automatically recognize human activity from input video stream using Histogram of Oriented Gradient
Pattern History (HOGPH) features and SVM classifier. For this purpose, the proposed system extracts
HOG features from a sequence of consecutive video frames and analyzes them to construct HOGPH feature
vector. The HOGPH feature vectors are used to train a multi-class SVM classifier for different human
activities. In test mode, we use the classifier with HOGPH feature vector to recognize human activity. We
have experimented with video data of human activity in real environments for three different tasks
(browsing, reading, and writing). The experimental result and its accuracy reveal that the proposed system
is applicable to recognize human activity in real-life.
An Efficient Activity Detection System based on Skeleton Joints Identification IJECEIAES
The increasing criminal activities in the current world has drawn lot of interest activity recognition techniques which helps to perform the sophistical analytical operations on human activity and also helps to interface the human and computer interactions. From the existing review analysis it is found that most of the existing systems are not emphasize on computational performance but are more application specific by identifying specific problems. Hence, it is found that all the features are not required for accurate and cost effective human activity detection. Thus, the human skelton action can be considered and presented a simple and accurate process to identify the significant joints only. From the outcomes it is found that the proposed system is cost effective and computational efficient activity recognition technique for human actions.
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.
Iris Encryption using (2, 2) Visual cryptography & Average Orientation Circul...AM Publications
Biometric authentication scheme used for person identification. Biometric authentication scheme consists of
uniqueness for identifying human using physiological and behavioral characteristics. So this technique is used for
criminal identification and this technique is used in civil service areas. In order to provide security to the data (2, 2)
secret sharing scheme. Basically iris recognition is the most secured scheme. Visual cryptography is the techniques
that divide the secret into shares.
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
ADVANCED FACE RECOGNITION FOR CONTROLLING CRIME USING PCAIAEME Publication
Face recognition has been a rapidly creating, testing and fascinating area with
respect to consistent applications. The task of face acknowledgment has been viably
asked about lately. With data and information gathering in abundance, there is an
urgent necessity for high security. Face acknowledgment has been a rapidly creating,
testing and interesting area concerning persistent applications. This paper gives a
cutting edge review of critical human face acknowledgment investigate
A virtual analysis on various techniques using ann with data miningeSAT Journals
Abstract In this paper, Firstly we discussed on monitoring the quality of video in network and proposing a tool called “VQMT” (Video Quality Measurement Tool) for automatic assessment of video quality and comparing it with MOS (mean opinion score). Secondly; author had proposed a tool called “ReGIMviZ” for video data visualization and personalization system based on semantic classification also used fuzzy logic. And lastly we focus on “SOFAIT” (SIFT and Optical flow affine image Transform) technique for face registration in video to improve action unit and its various algorithms. Here, in every system the common area is ANN architecture based on supervised learning algorithm.
MULTI SCALE ICA BASED IRIS RECOGNITION USING BSIF AND HOG sipij
Iris is a physiological biometric trait, which is unique among all biometric traits to recognize person
effectively. In this paper we propose Multi-scale Independent Component Analysis (ICA) based Iris
Recognition using Binarized Statistical Image Features (BSIF) and Histogram of Gradient orientation
(HOG). The Left and Right portion is extracted from eye images of CASIA V 1.0 database leaving top and
bottom portion of iris. The multi-scale ICA filter sizes of 5X5, 7X7 and 17X17 are used to correlate with
iris template to obtain BSIF. The HOGs are applied on BSIFs to extract initial features. The final feature is
obtained by fusing three HOGs. The Euclidian Distance is used to compare the final feature of database
image with test image final features to compute performance parameters. It is observed that the
performance of the proposed method is better compared to existing methods.
Detection and Tracking of Objects: A Detailed StudyIJEACS
Detecting and tracking objects are the most widespread and challenging tasks that a surveillance system must achieve to determine expressive events and activities, and automatically interpret and recover video content. An object can be a queue of people, a human, a head or a face. The goal of this article is to state the Detecting and tracking methods, classify them into different categories, and identify new trends, we introduce main trends and provide method to give a perception to fundamental ideas as well as to show their limitations in the object detection and tracking for more effective video analytics.
Optimized Biometric System Based on Combination of Face Images and Log Transf...sipij
The biometrics are used to identify a person effectively. In this paper, we propose optimised Face
recognition system based on log transformation and combination of face image features vectors. The face
images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation
is applied on enhanced image to generate features. The feature vectors of many images of a single person
image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is
used to compare test image feature vector with database feature vectors to identify a person. It is
experimented that, the performance of proposed algorithm is better compared to existing algorithms.
A presentation on Human Activity Recognition catered to the audience from an HCI or CS background. (Based on research by Bulling, A. et al. 2014. A tutorial on human activity recognition using body-worn inertial sensors. CSUR. 46, 3 (2014), 33.)
A Review on Feature Extraction Techniques and General Approach for Face Recog...Editor IJCATR
In recent time, alongwith the advances and new inventions in science and technology, fraud people and identity thieves are
also becoming smarter by finding new ways to fool the authorization and authentication process. So, there is a strong need of efficient
face recognition process or computer systems capable of recognizing faces of authenticated persons. One way to make face recognition
efficient is by extracting features of faces. Several feature extraction techniques are available such as template based, appearancebased,
geometry based, color segmentation based, etc. This paper presents an overview of various feature extraction techniques
followed in different reasearches for face recognition in the field of digital image processing and gives an approach for using these
feature extraction techniques for efficient face recognition
human activity recognization using machine learning with data analysisVenkat Projects
Human activity recognition, or HAR for short, is a broad field of study concerned with identifying the specific movement or action of a person based on sensor data.
The sensor data may be remotely recorded, such as video, radar, or other wireless methods. It contains data generated from accelerometer, gyroscope and other sensors of Smart phone to train supervised predictive models using machine learning techniques like SVM , Random forest and decision tree to generate a model. Which can be used to predict the kind of movement being carried out by the person, which is divided into six categories walking, walking upstairs, walking down-stairs, sitting, standing and laying?
MLM and SVM achieved accuracy of more than 99.2% in the original data set and 98.1% using new feature selection method. Results show that the proposed feature selection approach is a promising alternative to activity recognition on smart phones.
An Improved Self Organizing Feature Map Classifier for Multimodal Biometric R...ijtsrd
Multimodal biometric system is a system that is viable in authentication and capable of carrying the robustness of the system. Most existing biometric systems ear fingerprint and face ear suffer varying challenges such as large variability, high dimensionality, small sample size and average recognition time. These lead to the degrading performance and accuracy of the system. Sequel to this, multimodal biometric system was developed to overcome those challenges. The system was implemented in MATLAB environment. Am improved self organizing feature map was used to classify the fused features into known and unknown. The performance of the developed multimodal was evaluated based on sensitivity, recognition accuracy and time. Olabode, A. O | Amusan, D. G | Ajao, T. A "An Improved Self Organizing Feature Map Classifier for Multimodal Biometric Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26458.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26458/an-improved-self-organizing-feature-map-classifier-for-multimodal-biometric-recognition-system/olabode-a-o
Recently, the demand for surveillance system is increasing in real time application to enhance the security system. These surveillance systems are mainly used in crowded places such as shopping malls, sports stadium etc. In order to support enhance the security system, crowd behavior analysis has been proven a significant technique which is used for crowd monitoring, visual surveillance etc. For crowd behavior analysis, motion analysis is a crucial task which can be achieved with the help of trajectories and tracking of objects. Various approaches have been proposed for crowd behavior analysis which has limitation for densely crowded scenarios, a new object entering the scene etc. In this work, we propose a new approach for abnormal crowd behavior detection. Proposed approach is a motion based spatio-temporal feature analysis technique which is capable of obtaining trajectories of each detected object. We also present a technique to carry out the evaluation of individual object and group of objects by considering relational descriptors based on their environmental context. Finally, a classification is carried out for detection of abnormal or normal crowd behavior by following patch based process. In the results, we have reported that proposed model is able to achieve better performance when compared to existing techniques in terms of classification accuracy, true positive rate, and false positive rate.
Novel Approach to Use HU Moments with Image Processing Techniques for Real Ti...CSCJournals
Sign language is the fundamental communication method among people who suffer from speech and hearing defects. The rest of the world doesn’t have a clear idea of sign language. “Sign Language Communicator” (SLC) is designed to solve the language barrier between the sign language users and the rest of the world. The main objective of this research is to provide a low cost affordable method of sign language interpretation. This system will also be very useful to the sign language learners as they can practice the sign language. During the research available human computer interaction techniques in posture recognition was tested and evaluated. A series of image processing techniques with Hu-moment classification was identified as the best approach. To improve the accuracy of the system, a new approach; height to width ratio filtration was implemented along with Hu-moments. System is able to recognize selected Sign Language signs with the accuracy of 84% without a controlled background with small light adjustments.
A Complete Analysis of Human Action Recognition Proceduresijtsrd
Due to concerns like backdrop cluttering, incomplete obstruction, scale disparities, viewpoint, illumination, and appearance, identifying activities of humans from a sequence of video or still photos are a complex issue. Multiple movement recognition structures is necessary for numerous applications, such as a video investigation mechanism, human computer interface HCI , and robotics for characterising human behaviour. In this work, we bestow a comprehensive assessment of recent and advanced designs involved in the classification of human activity. We outline a classification of human activity approaches and go through their benefits and drawbacks. Specifically, we classify human activities categorization approaches into two broad classes based on if or not they make use of information from several modes. Next, each of these classes is broken down into its subclasses, which illustrate how each category models human activity. Monisa Nazir | Shalini Bhadola | Kirti Bhaia | Rohini Sharma "A Complete Analysis of Human Action Recognition Procedures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50522.pdf Paper URL: https://www.ijtsrd.com/computer-science/cognitive-science/50522/a-complete-analysis-of-human-action-recognition-procedures/monisa-nazir
Iris Encryption using (2, 2) Visual cryptography & Average Orientation Circul...AM Publications
Biometric authentication scheme used for person identification. Biometric authentication scheme consists of
uniqueness for identifying human using physiological and behavioral characteristics. So this technique is used for
criminal identification and this technique is used in civil service areas. In order to provide security to the data (2, 2)
secret sharing scheme. Basically iris recognition is the most secured scheme. Visual cryptography is the techniques
that divide the secret into shares.
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
ADVANCED FACE RECOGNITION FOR CONTROLLING CRIME USING PCAIAEME Publication
Face recognition has been a rapidly creating, testing and fascinating area with
respect to consistent applications. The task of face acknowledgment has been viably
asked about lately. With data and information gathering in abundance, there is an
urgent necessity for high security. Face acknowledgment has been a rapidly creating,
testing and interesting area concerning persistent applications. This paper gives a
cutting edge review of critical human face acknowledgment investigate
A virtual analysis on various techniques using ann with data miningeSAT Journals
Abstract In this paper, Firstly we discussed on monitoring the quality of video in network and proposing a tool called “VQMT” (Video Quality Measurement Tool) for automatic assessment of video quality and comparing it with MOS (mean opinion score). Secondly; author had proposed a tool called “ReGIMviZ” for video data visualization and personalization system based on semantic classification also used fuzzy logic. And lastly we focus on “SOFAIT” (SIFT and Optical flow affine image Transform) technique for face registration in video to improve action unit and its various algorithms. Here, in every system the common area is ANN architecture based on supervised learning algorithm.
MULTI SCALE ICA BASED IRIS RECOGNITION USING BSIF AND HOG sipij
Iris is a physiological biometric trait, which is unique among all biometric traits to recognize person
effectively. In this paper we propose Multi-scale Independent Component Analysis (ICA) based Iris
Recognition using Binarized Statistical Image Features (BSIF) and Histogram of Gradient orientation
(HOG). The Left and Right portion is extracted from eye images of CASIA V 1.0 database leaving top and
bottom portion of iris. The multi-scale ICA filter sizes of 5X5, 7X7 and 17X17 are used to correlate with
iris template to obtain BSIF. The HOGs are applied on BSIFs to extract initial features. The final feature is
obtained by fusing three HOGs. The Euclidian Distance is used to compare the final feature of database
image with test image final features to compute performance parameters. It is observed that the
performance of the proposed method is better compared to existing methods.
Detection and Tracking of Objects: A Detailed StudyIJEACS
Detecting and tracking objects are the most widespread and challenging tasks that a surveillance system must achieve to determine expressive events and activities, and automatically interpret and recover video content. An object can be a queue of people, a human, a head or a face. The goal of this article is to state the Detecting and tracking methods, classify them into different categories, and identify new trends, we introduce main trends and provide method to give a perception to fundamental ideas as well as to show their limitations in the object detection and tracking for more effective video analytics.
Optimized Biometric System Based on Combination of Face Images and Log Transf...sipij
The biometrics are used to identify a person effectively. In this paper, we propose optimised Face
recognition system based on log transformation and combination of face image features vectors. The face
images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation
is applied on enhanced image to generate features. The feature vectors of many images of a single person
image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is
used to compare test image feature vector with database feature vectors to identify a person. It is
experimented that, the performance of proposed algorithm is better compared to existing algorithms.
A presentation on Human Activity Recognition catered to the audience from an HCI or CS background. (Based on research by Bulling, A. et al. 2014. A tutorial on human activity recognition using body-worn inertial sensors. CSUR. 46, 3 (2014), 33.)
A Review on Feature Extraction Techniques and General Approach for Face Recog...Editor IJCATR
In recent time, alongwith the advances and new inventions in science and technology, fraud people and identity thieves are
also becoming smarter by finding new ways to fool the authorization and authentication process. So, there is a strong need of efficient
face recognition process or computer systems capable of recognizing faces of authenticated persons. One way to make face recognition
efficient is by extracting features of faces. Several feature extraction techniques are available such as template based, appearancebased,
geometry based, color segmentation based, etc. This paper presents an overview of various feature extraction techniques
followed in different reasearches for face recognition in the field of digital image processing and gives an approach for using these
feature extraction techniques for efficient face recognition
human activity recognization using machine learning with data analysisVenkat Projects
Human activity recognition, or HAR for short, is a broad field of study concerned with identifying the specific movement or action of a person based on sensor data.
The sensor data may be remotely recorded, such as video, radar, or other wireless methods. It contains data generated from accelerometer, gyroscope and other sensors of Smart phone to train supervised predictive models using machine learning techniques like SVM , Random forest and decision tree to generate a model. Which can be used to predict the kind of movement being carried out by the person, which is divided into six categories walking, walking upstairs, walking down-stairs, sitting, standing and laying?
MLM and SVM achieved accuracy of more than 99.2% in the original data set and 98.1% using new feature selection method. Results show that the proposed feature selection approach is a promising alternative to activity recognition on smart phones.
An Improved Self Organizing Feature Map Classifier for Multimodal Biometric R...ijtsrd
Multimodal biometric system is a system that is viable in authentication and capable of carrying the robustness of the system. Most existing biometric systems ear fingerprint and face ear suffer varying challenges such as large variability, high dimensionality, small sample size and average recognition time. These lead to the degrading performance and accuracy of the system. Sequel to this, multimodal biometric system was developed to overcome those challenges. The system was implemented in MATLAB environment. Am improved self organizing feature map was used to classify the fused features into known and unknown. The performance of the developed multimodal was evaluated based on sensitivity, recognition accuracy and time. Olabode, A. O | Amusan, D. G | Ajao, T. A "An Improved Self Organizing Feature Map Classifier for Multimodal Biometric Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26458.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26458/an-improved-self-organizing-feature-map-classifier-for-multimodal-biometric-recognition-system/olabode-a-o
Recently, the demand for surveillance system is increasing in real time application to enhance the security system. These surveillance systems are mainly used in crowded places such as shopping malls, sports stadium etc. In order to support enhance the security system, crowd behavior analysis has been proven a significant technique which is used for crowd monitoring, visual surveillance etc. For crowd behavior analysis, motion analysis is a crucial task which can be achieved with the help of trajectories and tracking of objects. Various approaches have been proposed for crowd behavior analysis which has limitation for densely crowded scenarios, a new object entering the scene etc. In this work, we propose a new approach for abnormal crowd behavior detection. Proposed approach is a motion based spatio-temporal feature analysis technique which is capable of obtaining trajectories of each detected object. We also present a technique to carry out the evaluation of individual object and group of objects by considering relational descriptors based on their environmental context. Finally, a classification is carried out for detection of abnormal or normal crowd behavior by following patch based process. In the results, we have reported that proposed model is able to achieve better performance when compared to existing techniques in terms of classification accuracy, true positive rate, and false positive rate.
Novel Approach to Use HU Moments with Image Processing Techniques for Real Ti...CSCJournals
Sign language is the fundamental communication method among people who suffer from speech and hearing defects. The rest of the world doesn’t have a clear idea of sign language. “Sign Language Communicator” (SLC) is designed to solve the language barrier between the sign language users and the rest of the world. The main objective of this research is to provide a low cost affordable method of sign language interpretation. This system will also be very useful to the sign language learners as they can practice the sign language. During the research available human computer interaction techniques in posture recognition was tested and evaluated. A series of image processing techniques with Hu-moment classification was identified as the best approach. To improve the accuracy of the system, a new approach; height to width ratio filtration was implemented along with Hu-moments. System is able to recognize selected Sign Language signs with the accuracy of 84% without a controlled background with small light adjustments.
A Complete Analysis of Human Action Recognition Proceduresijtsrd
Due to concerns like backdrop cluttering, incomplete obstruction, scale disparities, viewpoint, illumination, and appearance, identifying activities of humans from a sequence of video or still photos are a complex issue. Multiple movement recognition structures is necessary for numerous applications, such as a video investigation mechanism, human computer interface HCI , and robotics for characterising human behaviour. In this work, we bestow a comprehensive assessment of recent and advanced designs involved in the classification of human activity. We outline a classification of human activity approaches and go through their benefits and drawbacks. Specifically, we classify human activities categorization approaches into two broad classes based on if or not they make use of information from several modes. Next, each of these classes is broken down into its subclasses, which illustrate how each category models human activity. Monisa Nazir | Shalini Bhadola | Kirti Bhaia | Rohini Sharma "A Complete Analysis of Human Action Recognition Procedures" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50522.pdf Paper URL: https://www.ijtsrd.com/computer-science/cognitive-science/50522/a-complete-analysis-of-human-action-recognition-procedures/monisa-nazir
A Framework for Human Action Detection via Extraction of Multimodal FeaturesCSCJournals
This work discusses the application of an Artificial Intelligence technique called data extraction and a process-based ontology in constructing experimental qualitative models for video retrieval and detection. We present a framework architecture that uses multimodality features as the knowledge representation scheme to model the behaviors of a number of human actions in the video scenes. The main focus of this paper placed on the design of two main components (model classifier and inference engine) for a tool abbreviated as VASD (Video Action Scene Detector) for retrieving and detecting human actions from video scenes. The discussion starts by presenting the workflow of the retrieving and detection process and the automated model classifier construction logic. We then move on to demonstrate how the constructed classifiers can be used with multimodality features for detecting human actions. Finally, behavioral explanation manifestation is discussed. The simulator is implemented in bilingual; Math Lab and C++ are at the backend supplying data and theories while Java handles all front-end GUI and action pattern updating. To compare the usefulness of the proposed framework, several experiments were conducted and the results were obtained by using visual features only (77.89% for precision; 72.10% for recall), audio features only (62.52% for precision; 48.93% for recall) and combined audiovisual (90.35% for precision; 90.65% for recall).
There has been over the past few years, a very increased popularity for yoga. A lot of literatures have been published that claim yoga to be beneficial in improving the overall lifestyle and health especially in rehabilitation, mental health and more. Considering the fast-paced lives that individuals live, people usually prefer to exercise or work-out from the comfort of their homes and with that a need for an instructor arises. Hence why, we have developed a self-assisted system which can be used to detect and classify yoga asanas, which is discussed in-depth in this paper. Especially now when the pandemic has taken over the world, it is not feasible to attend physical classes or have an instructor over. Using the technology of Computer Vision, a computer-assisted system such as the one discussed, comes in very handy. The technologies such as ml5.js, PoseNet and Neural Networks are made use for the human pose estimation and classification. The proposed system uses the above-mentioned technologies to take in a real-time video input and analyze the pose of an individual, and classifies the poses into yoga asanas. It also displays the name of the yoga asana that is detected along with the confidence score.
Real-time Activity Recognition using Smartphone Accelerometerijtsrd
To identify the real time activities, an online algorithm need be considered. In this paper, we will first segment entire one activity as one time interval using Bayesian online detection method instead of fixed and small length time interval. Then, we introduce two layer random forest classification for real time activity recognition on the smartphone by embedded accelerometers. We evaluate the performance of our method based on six activities walking, upstairs, downstairs, sitting, standing, and laying on 30 volunteers. For the data considered, we get 92.4 overall accuracy based on six activities and 100 overall accuracy only based on dynamic activity and static activity. Shuang Na | Kandethody M. Ramachandran | Ming Ji | Yicheng Tu "Real-time Activity Recognition using Smartphone Accelerometer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29550.pdf Paper URL: https://www.ijtsrd.com/mathemetics/other/29550/real-time-activity-recognition-using-smartphone-accelerometer/shuang-na
BIOMETRIC AUTHORIZATION SYSTEM USING GAIT BIOMETRYIJCSEA Journal
ABSTRACT
Human gait, which is a new biometric aimed to recognize individuals by the way they walk have come to play an increasingly important role in visual surveillance applications. In this paper a novel hybrid holistic approach is proposed to show how behavioural walking characteristics can be used to recognize unauthorized and suspicious persons when they enter a surveillance area. Initially background is modelled from the input video captured from cameras deployed for security and the foreground moving object in the individual frames are segmented using the background subtraction algorithm. Then gait representing spatial, temporal and wavelet components are extracted and fused for training and testing multi class support vector machine models (SVM). The proposed system is evaluated using side view videos of NLPR database. The experimental results demonstrate that the proposed system achieves a pleasing recognition rate and also the results indicate that the classification ability of SVM with Radial Basis Function (RBF) is better than with other kernel functions.
Health monitoring catalogue based on human activity classification using mac...IJECEIAES
In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapped in the high dimensional data to two-dimensional data using t-distributed stochastic neighbor embedding technique. Overall, the first high dimensional data is visualized and compared with model’s performance with different algorithms and different number of coordinates.
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.
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.
Richard's aventures in two entangled wonderlandsRichard 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.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
ACTIVITY RECOGNITION USING HISTOGRAM OF ORIENTED GRADIENT PATTERN HISTORY
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
DOI : 10.5121/ijcseit.2014.4403 23
ACTIVITY RECOGNITION USING HISTOGRAM OF
ORIENTED GRADIENT PATTERN HISTORY
Dipankar Das
Department of Information and Communication Engineering, University of Rajshahi,
Rajshahi-6205, Bangladesh
ABSTRACT
Human activity recognition is an important task in computer vision because it has many application areas
such as, healthcare, security, entertainment, and tactical scenarios. This paper presents a methodology to
automatically recognize human activity from input video stream using Histogram of Oriented Gradient
Pattern History (HOGPH) features and SVM classifier. For this purpose, the proposed system extracts
HOG features from a sequence of consecutive video frames and analyzes them to construct HOGPH feature
vector. The HOGPH feature vectors are used to train a multi-class SVM classifier for different human
activities. In test mode, we use the classifier with HOGPH feature vector to recognize human activity. We
have experimented with video data of human activity in real environments for three different tasks
(browsing, reading, and writing). The experimental result and its accuracy reveal that the proposed system
is applicable to recognize human activity in real-life.
KEYWORDS
Human Activity, Activity Recognition, Histogram of Oriented Gradient, SVM.
1. INTRODUCTION
Recognizing human activity or task from real-time video data is one of the promising and
challenging applications of computer vision. Recently, this research has attracted the attention of
many researchers from different disciplines including Human-Computer-Interaction (HCI), and
Human-Robot-Interaction (HRI). The main objective of human task or activity recognition is to
provide useful information on a user’s behaviour that allows computing system or robot to
proactively assist users or to make a comfortable interaction with him/her [1]. Researchers in
computer vision and machine learning have investigated gestures and activity recognition from
static images and video in constrained environment or stationary settings[2][3][4]. However, there
are very limited number of works to recognize human task or activity in unconstrained daily life
settings. In this research, we recognize human task or acitivity in unconstrained real-world
environments, such as Internet browsing in an office environment, reading and writing in an
library environment etc., using Histogram of Oriented Gradient Pattern History (HOGPH) and
Support Vector Machine (SVM) classifier.
Human activity is very complex and diverse characteristics because an activity can be performed
in many different ways, depending on the different context and for a multitude of reasons.
Although some state-of-the-art system obtained good performance on many activity recognition
tasks, however, most of the researchers so far mainly focus on recognizing “which” activity is
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
24
being performed at a specific point in time. In contrast, only small number of researches
investigated means to extract qualitative information from the sensor data that allows us to infer
additional characteristics. It can be shown that such qualitative assessment are very difficult to
perform automatically, and has so far only been demonstrated for constrained settings, such as
sports[5][6]. For general activities or tasks, activity recognition research work is still far from
reaching a similar understanding. However, in this research, we proposed an activity recognition
system that tries to assess the qualitative characteristics of the human activity. For this purpose,
the proposed activity recognition system first extracts HOG features of activity during a certain
period of time to build up an activity pattern known as the histogram of oriented gradient pattern
history (HOGPH). Then the HOGPH feature vector are used to classify the human activity using
support vector machine (SVM) classifier.
To develop a good human activity recognition system, we need to develop a pattern recognition
system that is robust to intra-class variability. In activity, such type of intra-class variability exists
because same type of activity may be executed different ways by different person. Intra-class
variability can also happen if an activity is performed by the same person at different time. Thus,
for activity recognition, we need a classifier that adapts this type of intra-class variability. To
address this issue, the proposed system uses the multi-class SVM classifier with an increase
amount of training data for different activity classes using liblinear kernel. The proposed HOGPH
features are highly discriminative and person independent that also make help the system to adapt
intra-class variability.
2. RELATED WORKS
Since there are some successful researches in activity recognition, many researchers are
motivated toward more challenging and application-specific scenarios. Some real-world
application are benefited from task or activity recognition such as the office scenarios, the sports
and entertainment sector[7][8], the industrial sector[9][10], and healthcare. The activity of daily
living [11] attracted a significant amount of attention of the vision researchers [12] [13] [14]. The
traditional medical methods can be enhanced by analyzing the daily human activities [6]. Another
important aim of human activity analysis is to provide a healthy lifestyle. Thus many researchers
are encouraged to research in the related human activities, e.g. food intake [15] and
medication[16], brushing teeth or hand washing[17], or transportation routines[18].
Currently different companies or agencies use activity recognition as a key component in their
consumer products. For instant, to fundamentally change the game experience in game consoles
the NintendoWii or the Microsoft Kinect rely on the recognition of gestures or even full body
movements. Although they are mainly developed for the entertainment purpose, however, these
systems have used in other application areas, such as for personal fitness training and
rehabilitation, and also stimulated new activity recognition research [19]. These examples
highlight the importance of human activity or task recognition in both academic area and
industrial section. In spite of considerable improvement in inferring activities from on-body
inertial sensors and in prototyping and deploying activity recognition systems [20], developing
human activity recognition systems that meet application and user requirements remains a
challenging task. In this research, we recognize human task or acitivity in unconstrained real-
world environments, such as Internet browsing in an office environment, reading and writing in
an library environment etc., using Histogram of Oriented Gradient Pattern History (HOGPH) and
SVM classifer.
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
25
In human activity recognition system, Aggarwal et al.[21] propose state-space and template
matching based techniques. Some researchers use context-based decisions making techniques to
recognize human activity and action [22][23]. In [22], the authors propose an action recognition
technique that includes entering a room, using a computer, opening a file cabinet, picking up the
telephone, etc. In their system, they able to recognize actions based on prior knowledge about the
layout of the room. However, the system is limited to actions like sitting and standing. Also, it is
only able to recognize a picking action by knowledge of where the object is and tracking it after
the person has come within a certain distance of it. Moreover, both of the above contextual based
approaches require prior knowledge of the precise location of certain objects in the environment.
Davis et al. use temporal plates for matching and recognition of human activity [24]. The system
computes motion history images (MHI's) of the persons in the scene. Although template matching
procedures have a lower computational cost, they are usually more sensitive to the variance in the
duration of the movement.
3. PROPOSED APPROACH
The proposed human activity recognition system is illustrated in Figure 1 . In our approach, we
recoded the video data of human activity, such as browsing, reading, and writing, in a real-world
environment. The recorded video data is divided into two sections: training activity data, and
testing activity data. The proposed system is also divided into two phases: training and testing. In
training stage, we extract the histogram of oriented gradient features (HOG) [25] from each video
frame.
FIGURE 1: THE PROPOSED HUMAN ACTIVITY RECOGNITION APPROACH.
4. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
26
The HOG feature vectors from n consecutive video frames are analyzed to generate the histogram
of oriented gradient pattern history (HOGPH). The generated HOGPH feature vectors are used to
train the multi-class SVM classifier model for all activities. In testing stage, for each human
activity we generate the HOGPH feature vector and feed into the SVM model in recognition
mode. The model classifies the human activity into an appropriate class.
4. EXTRACTION OF HOG FEATURE
In this research, we use the histogram of oriented gradient (HOG) feature from each video frame
to represent human activity. The method is originally developed by N. Dalal and B. Triggs in [25]
for human detection purposes. Their method is based on evaluating well-normalized local
histograms of image gradient orientations in a dense grid. The fundamental principle is that local
object shape and appearance can often be represented rather well by the distribution of local
intensity gradients, even without precise knowledge of the corresponding gradient or edge
positions. In practical implementation, we divide the image window into small spatial regions
known as cell. Then for each cell, we build up a local 1-D histogram of gradient directions over
the pixels of the cell. The combined histogram entries form the gradient orientation
representation. The histogram is also useful to contrast-normalize the local responses to make it
invariance to illumination, and shadowing. This can be done by building up a measure of local
histogram energy over somewhat larger spatial regions (block) and using the results to normalize
all of the cells in the block. Here the normalized descriptor blocks are known as Histogram of
Oriented Gradient (HOG) descriptors.
5. CONSTRUCTION OF HOGPH FEATURE VECTOR
The human activity is determined by recognizing the pattern of task history in which the target
person is involved. For instance, if the target person is involved in a “reading” task, then the
pattern history such as “downward the head” indicates that his/her attention is toward the book.
Such type of activity related pattern is determined by analysing HOG feature vector as follows.
Given a video sequence, we extract the histogram of orientation gradient (HOG) feature for each
frame. The HOG features are combined for n consecutive frames to build a HOG feature pattern
history, HOGPH according to the following equation,
= +
6. where HF0 and HFi are the HOG features of the first and i-th frames, respectively. The first frame
captures the human appearance features involved in a task, and the rest of the HOG feature
frames indicate the change of behaviour pattern in the activity during the observation history.
Thus, the HOGPH feature captures the appearance of the activity and the activity related
behaviour pattern history. Here, each bin in the histogram represents the number of edges that
have the orientation within a given angular range. The angular range is set to 20 degrees and we
use unsigned gradient. Thus, the bin size is equal to180/20 = 9. With this bin size, if we create the
HOGPH feature vector for 10 consecutive video frames (i = 1,.....,9) then the HOGPH feature
vector is of size 90. A multi-class support vector machine (SVM) is learned using HOGPH
feature.
7. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
6. ACTIVITY CLASSIFICATION
In our activity recognition approach
learned with HOGPH features
descriptors are extracted from the
pattern history we analyze the subsequent HOG feature and calculate the difference of two
consecutive frames. Here the act
represented by orientations of an edge histogram within an object's subregion quantized into
bin and each edge's contribution is weighted by its magnitude. Therefore, each bin in the
histogram represents the number of edges that have orientations within a given angular range.
final pattern history of human activity is represented by the HOGPH feature vector.
class SVM classifier [26] is learned using
for our experiments in a multi-class mode with the
In the verification step, the HOGPH feature vector is extracted from activity video frames and
into the multi-class SVM classifier in recognition mode. Only the hypotheses for which a positive
confidence measurement is returned are kept for each
confidence level is recognized as the correct
probabilistic output of the SVM classifier.
7. EXPERIMENTAL RESULTS
We conducted experiments to investigate the performance of the vision
activity when s/he is involved in a task
7.1. Experimental Data
We implemented the proposed system and conducted experiments to verify
performance. To collect experimental data, we asked 14
females) for three different tasks: browsing, readin
Saitama University, Japan, with an average age of 27 years old. They did not receive any
information for their activity or task
average recorded lengths for each person for the
8, 9, and 9 minutes, respectively.
were involved in browsing, reading and writing tasks, respectively.
Figure2: Human involved in activities
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
LASSIFICATION
activity recognition approach, a multi-class support vector machine (SVM) classifier is
features vector. To represent the appearance of an activity
descriptors are extracted from the first frame of an activity image. In order to describe the
pattern history we analyze the subsequent HOG feature and calculate the difference of two
activity pattern is represented by its local shape. The local shape is
represented by orientations of an edge histogram within an object's subregion quantized into
bin and each edge's contribution is weighted by its magnitude. Therefore, each bin in the
togram represents the number of edges that have orientations within a given angular range.
final pattern history of human activity is represented by the HOGPH feature vector.
is learned using HOGPH feature vector. We use the LIBSVM
class mode with the liblinear and rbf exponential kernel
the HOGPH feature vector is extracted from activity video frames and
class SVM classifier in recognition mode. Only the hypotheses for which a positive
confidence measurement is returned are kept for each activity. Activity with
as the correct activity. The confidence level is measured using the
probabilistic output of the SVM classifier.
ESULTS
We conducted experiments to investigate the performance of the vision-based system to
activity when s/he is involved in a task.
We implemented the proposed system and conducted experiments to verify activity recognition
. To collect experimental data, we asked 14-non-paid participants (11 males and 3
females) for three different tasks: browsing, reading, and writing. They were graduate students at
with an average age of 27 years old. They did not receive any
activity or task. All the tasks were recorded using a video camera. The
or each person for the activities of browsing, reading, and writing
8, 9, and 9 minutes, respectively. Figure 2 show some snapshot of activities where the humans
involved in browsing, reading and writing tasks, respectively.
involved in activities browsing, reading, and writing, respectively
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
27
class support vector machine (SVM) classifier is
activity, HOG
. In order to describe the activity
pattern history we analyze the subsequent HOG feature and calculate the difference of two
. The local shape is
represented by orientations of an edge histogram within an object's subregion quantized into K-
bin and each edge's contribution is weighted by its magnitude. Therefore, each bin in the
togram represents the number of edges that have orientations within a given angular range. The
final pattern history of human activity is represented by the HOGPH feature vector. The multi-
use the LIBSVM package
exponential kernels.
the HOGPH feature vector is extracted from activity video frames and fed
class SVM classifier in recognition mode. Only the hypotheses for which a positive
with the highest
idence level is measured using the
based system to human
activity recognition
paid participants (11 males and 3
g, and writing. They were graduate students at
with an average age of 27 years old. They did not receive any
. All the tasks were recorded using a video camera. The
of browsing, reading, and writing were
show some snapshot of activities where the humans
, respectively.
8. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
28
7.2. Training and Testing the SVM Classifier
Each of the recorded tasks was divided into test and train sample videos. As described in Section
5, one sample features a vector consisting of ten frames of the video stream. Table 1 shows the
number of training and testing sample used in the experiments and their total length for three
different activities. The multi-class SVM classifier was learned using the training samples for
three activities. In the recognition mode, each test sample value was tested against three tasks and
the task with the highest probability was selected as the detected task. Total 8740 test samples for
three activities were used to test the system. The SVM classifier was used with two different
kernels: liblinear and rbf. The performance of the classifier was compared for different kernels on
the proposed activity test sample dataset.
Table1: Sample Training and Test Data for Experiments
Activity Training Data Test Data
Length
(in Min)
No. of
Samples
Length
(in Min)
No. of Samples
Browsing 19.30 2482 22.93 3104
Reading 16.00 2058 19.10 2904
Writing 15.00 1929 19.91 2732
Total 50.30 6469 61.94 8740
7.3. Activity Recognition Performance
We measured the activity recognition performance on the test video data. From the test video
data, we used 8740 test activity samples. First, we measured the performance of the SVM
classifier with rbf exponential kernel. In this case, among 8740 test samples 6064 activity samples
were correctly recognized with 69.38% accuracy. Table 2 shows the activity specific recognition
performance and cross category confusion of activity recognition system using the rbf
exponential kernel.
Table 2: Activity Recognition Performance Using SVM Classifier and rbf Exponential Kernel
Activity Browsing Reading Writing
Browsing 2439 302 363
Reading 318 1935 651
Writing 278 764 1690
Second, the performance of the proposed system was also measured using SVM classifier and
liblinear kernel. Here, among 8740 test samples 8277 activity samples were correctly recognized
with 94.70% recognition accuracy. Table 3 shows the activity specific recognition performance
and cross category confusion of activity recognition system using the liblinear kernel. From the
experimental result, it was shown that the proposed system performed significant improvement
for human activity recognition task with liblinear kernel than rbf kernel. The liblinear kernel was
also decreased the cross-category confusion rate. Figure 3 shows some snapshots of the
recognized human activities for “browsing”, “reading”, and “writing”, respectively.
9. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
29
Table 3: Activity Recognition Performance Using SVM Classifier and liblinear Kernel
Activity Browsing Reading Writing
Browsing 3101 3 0
Reading 28 2448 428
Writing 0 4 2728
FIGURE 3: SNAPSHOT OF RECOGNIZED ACTIVITIES
8. CONCLUSION
In this paper, we propose an automatic human activity recognition system that can accurately
recognize three different activities (browsing, reading, and writing) from real-life video data. The
proposed system uses the HOG pattern history (HOGPH) feature vectors that represent human
activity and its behavior accurately and precisely. We analyze and combine the HOG features of
human activity for few consecutive video frames. The first frame represents appearance of the
activity and the remaining frames indicate the change of behavior pattern during activity. The
HOGPH feature vectors are used to learn and classify human activity using SVM classifier. In
this research, the performance of the classifier is compared using two different kernels: rbf and
liblinear. The experimental result shows that the proposed system produces higher accuracy with
10. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
30
liblinear kernel than rbf kernel. In future, we want to extend and compare the proposed system
for human activity recognition with more versatile databases.
REFERENCES
[1] G. D. Abowd, A. K. Dey, R. Orr and J. Brotherton (1998), “Context-awareness in wearable and
ubiquitous computing,” Virtual Reality, vol. 3, no. 3, pp. 200-211.
[2] S. Mitra and T. Acharya (2007), “Gesture Recognition: A Survey,” IEEE Trans. on Systems, Man,
and Cybernetics, Part C: Applications and Reviews, vol. 37, no. 3, pp. 311-324.
[3] P. Turaga, R. Chellappa, V. S. Subrahmanian and O. Udrea (2008), “ Machine Recognition of Human
Activities: A Survey,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 18, no. 11,
pp. 1473-1488.
[4] J. K. Aggarwal and M. S. Ryoo (2011), “ Human activity analysis: A review,” Comput. Surveys , vol.
43, no. 3, pp. 16:1-16:43.
[5] E. Velloso, A. Bulling, H. Gellersen, W. Ugulino and H. Fuks (2013), “Qualitative Activity
Recognition of Weight Lifting Exercises,” in 4th Augmented Human International Conference
(AugmentedHuman 2013).
[6] B. Tessendorf, F. Gravenhorst, B. Arnrich and G. Troster (2011), “An IMU-based Sensor Network to
Continuously Monitor Rowing Technique on the Water,” in ISSNIP.
[7] K. Kunze, M. Barry, E. Heinz, P. Lukowicz, D. Majoe and J. Gutknecht (2006), “Towards
Recognizing Tai Chi–An Initial Experiment Using Wearable Sensors,” in FAWC.
[8] D. Minnen, T. Starner, I. Essa and C. Isbell (2006), “Discovering Characteristic Actions from On-
Body Sensor Data,” in 10th IEEE International Symposium on Wearable Computers (ISWC).
[9] I. Maurtua, P. T. Kirisci, T. Stiefmeier, M. L. Sbodio and H.Witt (2007), “A Wearable Computing
Prototype for Supporting Training Activities in Automative Production,” in 4th International Forum
on Applied Wearable Computing.
[10] T. Stiefmeier, D. Roggen, G. Ogris, P. Lukowicz and G. Troster (2008), “Wearable Activity Tracking
in Car Manufacturing,” IEEE Pervasive Computing, vol. 7, no. 2, pp. 42-50.
[11] S. Katz, T. Downs, H. Cash and R. Grotz (1970), “ Progress in development of the index of ADL,”
The Gerontologist, vol. 10, no. 1, p. 20.
[12] L. Bao and S. S. Intille (2004), “Activity Recognition from User-Annotated Acceleration Data,” in
Pervasive.
[13] N. Ravi, N. Dandekar, P. Mysore and M. L. Littman (2005), “Activity recognition from accelerometer
data,” in 17th International Conference on Innovative Applications of Artificial Intelligence.
[14] B. Logan, J. Healey, M. Philipose, E. Tapia and S. Intille (2007), “A long-term evaluation of sensing
modalities for activity recognition,” in UbiComp.
[15] G. Pirkl, K. Stockinger, K. Kunze and P. Lukowicz (2008), “Adapting magnetic resonant coupling
based relative positioning technology for wearable activitiy recogniton,” in ISWC.
[16] R. d. Oliveira, M. Cherubini and N. Oliver (2010), “MoviPill: Improving Medication Compliance for
Elders Using a Mobile Persuasive Social Game,” in UbiComp.
[17] J. Lester, T. Choudhury and G. Borriello (2006), “A Practical Approach to Recognizing Physical
Activities,” in International Conference on Pervasive Computing.
[18] J. Krumm and E. Horvitz (2006), “Predestination: Inferring Destinations from Partial Trajectories,” in
UbiComp.
[19] J. Sung, C. Ponce, B. Selman and A. Saxena (2011), “ Human Activity Detection from RGBD
Images,” in AAAI Workshop on Plan, Activity, and Intent Recognition.
[20] D. Ashbrook and T. Starner (2010), “MAGIC: a motion gesture design tool,” in CHI.
[21] J. K. Aggarwal and Q. Cai (1999), “ Human motion analysis:A review,” Computer Vision and Image
Understanding, pp. 428-440.
[22] D. Ayers and M. Shah (1998), “Recognizing human action in a static room,” in Computer Vision and
Pattern Recognition.
[23] S. S. Intille, J. Davis and A. Bobick (1997), “Real time closed world tracking,” in IEEE International
Conference on Computer Vision and Pattern Recognition.
[24] J. Davis and A. Bobick (1997), “The representation and recognition of action using temporal plates,”
in Computer Vision and Pattern Recognition.
11. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
31
[25] N. Dalal and B. Triggs (2005), “Histograms of oriented gradients for human detection,” in CVPR (1).
[26] I. Tsochantaridis, T. Joachims, T. Hofmann and Y. Altun (2005), “Large margin methods for
structured and interdependent output variables,” Journal of Machine Learning Research, vol. 6, pp.
1453-1484.
Authors
Dipankar Das received his B.Sc. and M.Sc. degree in Computer Science and Technology
from the University of Rajshahi, Rajshahi, Bangladesh in 1996 and 1997, respectively. He
also received his PhD degree in Computer Vision from Saitama University Japan in 2010.
He was a Postdoctoral fellow in Robot Vision from October 2011 to March 2014 at the
same university. He is currently working as an associate professor of the Department of
Information and Communication Engineering, University of Rajshahi. His research
interests include Object Recognition and Human Computer Interaction.