Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects
and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries
could potentially provide useful information about the scene’s structure and composition. A novel framework for blob based occluded
object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in
video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled
by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of
Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and
tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several
experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and
effectiveness of the proposed method.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
This is a presentation on the Yolo(You Only Look Once) object detection system. This is a state-of-the-art system that is works very fast. The presentation has been derived from the paper cited below
@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
This is a presentation on the Yolo(You Only Look Once) object detection system. This is a state-of-the-art system that is works very fast. The presentation has been derived from the paper cited below
@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}
Slides from the UPC reading group on computer vision about the following paper:
Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Object detection is an important computer vision technique with applications in several domains such as autonomous driving, personal and industrial robotics. The below slides cover the history of object detection from before deep learning until recent research. The slides aim to cover the history and future directions of object detection, as well as some guidelines for how to choose which type of object detector to use for your own project.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
Slides from the UPC reading group on computer vision about the following paper:
Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Object detection is an important computer vision technique with applications in several domains such as autonomous driving, personal and industrial robotics. The below slides cover the history of object detection from before deep learning until recent research. The slides aim to cover the history and future directions of object detection, as well as some guidelines for how to choose which type of object detector to use for your own project.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
An optimized framework for detection and tracking of video objects in challen...ijma
Segmentation and tracking are two important aspects in visual surveillance systems. Many barriers such as
cluttered background, camera movements, and occlusion make the robust detection and tracking a difficult
problem, especially in case of multiple moving objects. Object detection in the presence of camera noise
and with variable or unfavourable luminance conditions is still an active area of research. This paper
propose a framework which can effectively detect the moving objects and track them despite of occlusion
and a priori knowledge of objects in the scene. The segmentation step uses a robust threshold decision
algorithm which uses a multi-background model. The video object tracking is able to track multiple objects
along with their trajectories based on Continuous Energy Minimization. In this work, an effective
formulation of multi-target tracking as minimization of a continuous energy is combined with multibackground
registration. Apart from the recent approaches, it focus on making use of an energy that
corresponds to a more complete representation of the problem, rather than one that is amenable to global
optimization. Besides the image evidence, the energy function considers physical constraints, such as target
dynamics, mutual exclusion, and track persistence. The proposed tracking framework is able to track
multiple objects despite of occlusions under dynamic background conditions.
Proposed Multi-object Tracking Algorithm Using Sobel Edge Detection operatorQUESTJOURNAL
ABSTRACT:Tracking of moving objects that is called video tracking is used for measuring motion parameters and obtaining a visual record of the moving objects, it is an important area of application in image processing. In general there are two different approaches to obtain object tracking: the first is Recognition-based Tracking, and the second is the Motion-based Tracking. Video tracking system raises a wide possibility in today’s society. This system is used in various applications such as military, security, monitoring, robotic, and nowadays in dayto-day applications. However the video tracking systems still have many open problems and various research activities in a video tracking system are explores. This paper presents an algorithm for video tracking of any moving targets with the uses of contour based detection technique that depends on the sobel operator. The proposed system is suitable for indoor and outdoor applications. Our approach has the advantage of extending the applicability of tracking system and also, as presented here improves the performance of the tracker making feasible high frame rate video tracking. The goal of the tracking system is to analyze the video frames and estimate the position of a part of the input video frame (usually a moving object), our approach can detect, tracked any object more than one object and calculate the position of the moving objects. Therefore, the aim of this paper is to construct a motion tracking system for moving objects. Where, at the end of this paper, the detail outcome and result are discussed using experiments results of the proposed technique
MULTIPLE OBJECTS TRACKING IN SURVEILLANCE VIDEO USING COLOR AND HU MOMENTSsipij
Multiple objects tracking finds its applications in many high level vision analysis like object behaviour
interpretation and gait recognition. In this paper, a feature based method to track the multiple moving
objects in surveillance video sequence is proposed. Object tracking is done by extracting the color and Hu
moments features from the motion segmented object blob and establishing the association of objects in the
successive frames of the video sequence based on Chi-Square dissimilarity measure and nearest neighbor
classifier. The benchmark IEEE PETS and IEEE Change Detection datasets has been used to show the
robustness of the proposed method. The proposed method is assessed quantitatively using the precision and
recall accuracy metrics. Further, comparative evaluation with related works has been carried out to exhibit
the efficacy of the proposed method.
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.
A Critical Survey on Detection of Object and Tracking of Object With differen...Editor IJMTER
Basically object detection and object tracking are two important and challenging aspects in
many computer vision applications like surveillance system, vehicle navigation, autonomous robot
navigation, compression of video etc. Object detection is first low level important task for any video
surveillance application. To detection of moving object is a challenging task. Tracking is required in
higher level applications that required the location and shape of object. There are three key steps in
video analysis: detection of interesting moving objects, tracking of such objects from frame to frame,
and analysis of object tracks to recognize their behavior. Object detection and tracking especially for
human and vehicle is currently most active research topic. A lot of research has been undergoing
ranging from applications to noble algorithms. The main objective of this paper is to review (survey)
of various moving object detection and object tracking methodologies.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...csandit
Motion detection and object segmentation are an important research area of image-video
processing and computer vision. The technique and mathematical modeling used to detect and
segment region of interest (ROI) objects comprise the algorithmic modules of various high-level
techniques in video analysis, object extraction, classification, and recognition. The detection of
moving object is significant in many tasks, such as video surveillance & moving object tracking.
The design of a video surveillance system is directed on involuntary identification of events of
interest, especially on tracking and on classification of moving objects. An entropy based realtime
adaptive non-parametric window thresholding algorithm for change detection is
anticipated in this research. Based on the approximation of the value of scatter of sections of
change in a difference image, a threshold of every image block is calculated discriminatively
using entropy structure, and then the global threshold is attained by averaging all thresholds for
image blocks of the frame. The block threshold is calculated contrarily for regions of change
and background. Investigational results show the proposed thresholding algorithm
accomplishes well for change detection with high efficiency.
Real Time Object Identification for Intelligent Video Surveillance ApplicationsEditor IJCATR
Intelligent video surveillance system has emerged as a very important research topic in the computer vision field in the
recent years. It is well suited for a broad range of applications such as to monitor activities at traffic intersections for detecting
congestions and predict the traffic flow. Object classification in the field of video surveillance is a key component of smart
surveillance software. Two robust methodology and algorithms adopted for people and object classification for automated surveillance
systems is proposed in this paper. First method uses background subtraction model for detecting the object motion. The background
subtraction and image segmentation based on morphological transformation for tracking and object classification on highways is
proposed. This algorithm uses erosion followed by dilation on various frames. Proposed algorithm in first method, segments the image
by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more
accurately. The system used in second method adopts the object detection method without background subtraction because of the static
object detection. Segmentation is done by the bounding box registration technique. Then the classification is done with the multiclass
SVM using the edge histogram as features. The edge histograms are calculated for various bin values in different environment. The
result obtained demonstrates the effectiveness of the proposed approach.
Java Implementation based Heterogeneous Video Sequence Automated Surveillance...CSCJournals
Automated video based surveillance monitoring is an essential and computationally challenging task to resolve issues in the secure access localities. This paper deals with some of the issues which are encountered in the integration surveillance monitoring in the real-life circumstances. We have employed video frames which are extorted from heterogeneous video formats. Each video frame is chosen to identify the anomalous events which are occurred in the sequence of time-driven process. Background subtraction is essentially required based on the optimal threshold and reference frame. Rest of the frames are ablated from reference image, hence all the foreground images paradigms are obtained. The co-ordinate existing in the deducted images is found by scanning the images horizontally until the occurrence of first black pixel. Obtained coordinate is twinned with existing co-ordinates in the primary images. The twinned co-ordinate in the primary image is considered as an active-region-of-interest. At the end, the starred images are converted to temporal video that scrutinizes the moving silhouettes of human behaviors in a static background. The proposed model is implemented in Java. Results and performance analysis are carried out in the real-life environments.
DEEP LEARNING APPROACH FOR EVENT MONITORING SYSTEMIJMIT JOURNAL
With an increasing number of extreme events and complexity, more alarms are being used to monitor
control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable
actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It
is important to have a rigid surveillance that should guarantee protection from any sought of hazard.
Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these
CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired
off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with
a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing
images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and
a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on
providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO
algorithm, it divides an image from the video into grid system and each grid detects an object within itself
Analysis of Human Behavior Based On Centroid and Treading TrackIJMER
Human body motion analysis is an important technology which modem bio-mechanics
combines with computer vision and has been widely used in intelligent control, human computer
interaction, motion analysis, and virtual reality and other fields. In which the moving human body
detection is the most important part of the human body motion analysis, the purpose is to detect the
moving human body with its behavior from the background image in video sequences, and for the follow-up treatment such as the target classification, the human body tracking and behavior understanding, its
effective detection plays a very important role
This paper contain the study about vibration analysis for gearbox casing using finite element analysis
(FEA).The aim of this paper is to apply ANSYS software to determine the natural frequency of gearbox casing. The
objective of the project is to analyze differential gearbox casing of tata indigo cs vehicle for modal and stress
analysis. The theoretical modal analysis needs to be validated with experimental results from Fourier frequency
transformer (FFT) analysis. The main motivation behind the work is to go for a complete FEA of casing rather than
empirical formulae and iterative procedures.
This paper represents a survey of various methods of video surveillance system which improves the security. The aim of this paper is to review of various moving object detection technics. This paper focuses on detection of moving objects in video surveillance system. Moving body detection is first important task for any video surveillance system. Detection of moving object is a challenging task. Tracking is required in higher level applications that require the location and shape of object in every frame. In this survey,paper described about optical flow method, Background subtraction, frame differencing to detect moving object. It also described tracking method based on Morphology technique.
Keywords -- Frame separation, Pre-processing, Object detection using frame difference, Optical flow,
Temporal Differencing and background subtraction. Object tracking
Text Mining in Digital Libraries using OKAPI BM25 ModelEditor IJCATR
The emergence of the internet has made vast amounts of information available and easily accessible online. As a result, most libraries have digitized their content in order to remain relevant to their users and to keep pace with the advancement of the internet. However, these digital libraries have been criticized for using inefficient information retrieval models that do not perform relevance ranking to the retrieved results. This paper proposed the use of OKAPI BM25 model in text mining so as means of improving relevance ranking of digital libraries. Okapi BM25 model was selected because it is a probability-based relevance ranking algorithm. A case study research was conducted and the model design was based on information retrieval processes. The performance of Boolean, vector space, and Okapi BM25 models was compared for data retrieval. Relevant ranked documents were retrieved and displayed at the OPAC framework search page. The results revealed that Okapi BM 25 outperformed Boolean model and Vector Space model. Therefore, this paper proposes the use of Okapi BM25 model to reward terms according to their relative frequencies in a document so as to improve the performance of text mining in digital libraries.
Green Computing, eco trends, climate change, e-waste and eco-friendlyEditor IJCATR
This study focused on the practice of using computing resources more efficiently while maintaining or increasing overall performance. Sustainable IT services require the integration of green computing practices such as power management, virtualization, improving cooling technology, recycling, electronic waste disposal, and optimization of the IT infrastructure to meet sustainability requirements. Studies have shown that costs of power utilized by IT departments can approach 50% of the overall energy costs for an organization. While there is an expectation that green IT should lower costs and the firm’s impact on the environment, there has been far less attention directed at understanding the strategic benefits of sustainable IT services in terms of the creation of customer value, business value and societal value. This paper provides a review of the literature on sustainable IT, key areas of focus, and identifies a core set of principles to guide sustainable IT service design.
Policies for Green Computing and E-Waste in NigeriaEditor IJCATR
Computers today are an integral part of individuals’ lives all around the world, but unfortunately these devices are toxic to the environment given the materials used, their limited battery life and technological obsolescence. Individuals are concerned about the hazardous materials ever present in computers, even if the importance of various attributes differs, and that a more environment -friendly attitude can be obtained through exposure to educational materials. In this paper, we aim to delineate the problem of e-waste in Nigeria and highlight a series of measures and the advantage they herald for our country and propose a series of action steps to develop in these areas further. It is possible for Nigeria to have an immediate economic stimulus and job creation while moving quickly to abide by the requirements of climate change legislation and energy efficiency directives. The costs of implementing energy efficiency and renewable energy measures are minimal as they are not cash expenditures but rather investments paid back by future, continuous energy savings.
Performance Evaluation of VANETs for Evaluating Node Stability in Dynamic Sce...Editor IJCATR
Vehicular ad hoc networks (VANETs) are a favorable area of exploration which empowers the interconnection amid the movable vehicles and between transportable units (vehicles) and road side units (RSU). In Vehicular Ad Hoc Networks (VANETs), mobile vehicles can be organized into assemblage to promote interconnection links. The assemblage arrangement according to dimensions and geographical extend has serious influence on attribute of interaction .Vehicular ad hoc networks (VANETs) are subclass of mobile Ad-hoc network involving more complex mobility patterns. Because of mobility the topology changes very frequently. This raises a number of technical challenges including the stability of the network .There is a need for assemblage configuration leading to more stable realistic network. The paper provides investigation of various simulation scenarios in which cluster using k-means algorithm are generated and their numbers are varied to find the more stable configuration in real scenario of road.
Optimum Location of DG Units Considering Operation ConditionsEditor IJCATR
The optimal sizing and placement of Distributed Generation units (DG) are becoming very attractive to researchers these days. In this paper a two stage approach has been used for allocation and sizing of DGs in distribution system with time varying load model. The strategic placement of DGs can help in reducing energy losses and improving voltage profile. The proposed work discusses time varying loads that can be useful for selecting the location and optimizing DG operation. The method has the potential to be used for integrating the available DGs by identifying the best locations in a power system. The proposed method has been demonstrated on 9-bus test system.
Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classifi...Editor IJCATR
Early detection of diabetes mellitus (DM) can prevent or inhibit complication. There are several laboratory test that must be done to detect DM. The result of this laboratory test then converted into data training. Data training used in this study generated from UCI Pima Database with 6 attributes that were used to classify positive or negative diabetes. There are various classification methods that are commonly used, and in this study three of them were compared, which were fuzzy KNN, C4.5 algorithm and Naïve Bayes Classifier (NBC) with one identical case. The objective of this study was to create software to classify DM using tested methods and compared the three methods based on accuracy, precision, and recall. The results showed that the best method was Fuzzy KNN with average and maximum accuracy reached 96% and 98%, respectively. In second place, NBC method had respective average and maximum accuracy of 87.5% and 90%. Lastly, C4.5 algorithm had average and maximum accuracy of 79.5% and 86%, respectively.
Web Scraping for Estimating new Record from Source SiteEditor IJCATR
Study in the Competitive field of Intelligent, and studies in the field of Web Scraping, have a symbiotic relationship mutualism. In the information age today, the website serves as a main source. The research focus is on how to get data from websites and how to slow down the intensity of the download. The problem that arises is the website sources are autonomous so that vulnerable changes the structure of the content at any time. The next problem is the system intrusion detection snort installed on the server to detect bot crawler. So the researchers propose the use of the methods of Mining Data Records and the method of Exponential Smoothing so that adaptive to changes in the structure of the content and do a browse or fetch automatically follow the pattern of the occurrences of the news. The results of the tests, with the threshold 0.3 for MDR and similarity threshold score 0.65 for STM, using recall and precision values produce f-measure average 92.6%. While the results of the tests of the exponential estimation smoothing using ? = 0.5 produces MAE 18.2 datarecord duplicate. It slowed down to 3.6 datarecord from 21.8 datarecord results schedule download/fetch fix in an average time of occurrence news.
Evaluating Semantic Similarity between Biomedical Concepts/Classes through S...Editor IJCATR
Most of the existing semantic similarity measures that use ontology structure as their primary source can measure semantic similarity between concepts/classes using single ontology. The ontology-based semantic similarity techniques such as structure-based semantic similarity techniques (Path Length Measure, Wu and Palmer’s Measure, and Leacock and Chodorow’s measure), information content-based similarity techniques (Resnik’s measure, Lin’s measure), and biomedical domain ontology techniques (Al-Mubaid and Nguyen’s measure (SimDist)) were evaluated relative to human experts’ ratings, and compared on sets of concepts using the ICD-10 “V1.0” terminology within the UMLS. The experimental results validate the efficiency of the SemDist technique in single ontology, and demonstrate that SemDist semantic similarity techniques, compared with the existing techniques, gives the best overall results of correlation with experts’ ratings.
Semantic Similarity Measures between Terms in the Biomedical Domain within f...Editor IJCATR
The techniques and tests are tools used to define how measure the goodness of ontology or its resources. The similarity between biomedical classes/concepts is an important task for the biomedical information extraction and knowledge discovery. However, most of the semantic similarity techniques can be adopted to be used in the biomedical domain (UMLS). Many experiments have been conducted to check the applicability of these measures. In this paper, we investigate to measure semantic similarity between two terms within single ontology or multiple ontologies in ICD-10 “V1.0” as primary source, and compare my results to human experts score by correlation coefficient.
A Strategy for Improving the Performance of Small Files in Openstack Swift Editor IJCATR
This is an effective way to improve the storage access performance of small files in Openstack Swift by adding an aggregate storage module. Because Swift will lead to too much disk operation when querying metadata, the transfer performance of plenty of small files is low. In this paper, we propose an aggregated storage strategy (ASS), and implement it in Swift. ASS comprises two parts which include merge storage and index storage. At the first stage, ASS arranges the write request queue in chronological order, and then stores objects in volumes. These volumes are large files that are stored in Swift actually. During the short encounter time, the object-to-volume mapping information is stored in Key-Value store at the second stage. The experimental results show that the ASS can effectively improve Swift's small file transfer performance.
Integrated System for Vehicle Clearance and RegistrationEditor IJCATR
Efficient management and control of government's cash resources rely on government banking arrangements. Nigeria, like many low income countries, employed fragmented systems in handling government receipts and payments. Later in 2016, Nigeria implemented a unified structure as recommended by the IMF, where all government funds are collected in one account would reduce borrowing costs, extend credit and improve government's fiscal policy among other benefits to government. This situation motivated us to embark on this research to design and implement an integrated system for vehicle clearance and registration. This system complies with the new Treasury Single Account policy to enable proper interaction and collaboration among five different level agencies (NCS, FRSC, SBIR, VIO and NPF) saddled with vehicular administration and activities in Nigeria. Since the system is web based, Object Oriented Hypermedia Design Methodology (OOHDM) is used. Tools such as Php, JavaScript, css, html, AJAX and other web development technologies were used. The result is a web based system that gives proper information about a vehicle starting from the exact date of importation to registration and renewal of licensing. Vehicle owner information, custom duty information, plate number registration details, etc. will also be efficiently retrieved from the system by any of the agencies without contacting the other agency at any point in time. Also number plate will no longer be the only means of vehicle identification as it is presently the case in Nigeria, because the unified system will automatically generate and assigned a Unique Vehicle Identification Pin Number (UVIPN) on payment of duty in the system to the vehicle and the UVIPN will be linked to the various agencies in the management information system.
Assessment of the Efficiency of Customer Order Management System: A Case Stu...Editor IJCATR
The Supermarket Management System deals with the automation of buying and selling of good and services. It includes both sales and purchase of items. The project Supermarket Management System is to be developed with the objective of making the system reliable, easier, fast, and more informative.
Energy-Aware Routing in Wireless Sensor Network Using Modified Bi-Directional A*Editor IJCATR
Energy is a key component in the Wireless Sensor Network (WSN)[1]. The system will not be able to run according to its function without the availability of adequate power units. One of the characteristics of wireless sensor network is Limitation energy[2]. A lot of research has been done to develop strategies to overcome this problem. One of them is clustering technique. The popular clustering technique is Low Energy Adaptive Clustering Hierarchy (LEACH)[3]. In LEACH, clustering techniques are used to determine Cluster Head (CH), which will then be assigned to forward packets to Base Station (BS). In this research, we propose other clustering techniques, which utilize the Social Network Analysis approach theory of Betweeness Centrality (BC) which will then be implemented in the Setup phase. While in the Steady-State phase, one of the heuristic searching algorithms, Modified Bi-Directional A* (MBDA *) is implemented. The experiment was performed deploy 100 nodes statically in the 100x100 area, with one Base Station at coordinates (50,50). To find out the reliability of the system, the experiment to do in 5000 rounds. The performance of the designed routing protocol strategy will be tested based on network lifetime, throughput, and residual energy. The results show that BC-MBDA * is better than LEACH. This is influenced by the ways of working LEACH in determining the CH that is dynamic, which is always changing in every data transmission process. This will result in the use of energy, because they always doing any computation to determine CH in every transmission process. In contrast to BC-MBDA *, CH is statically determined, so it can decrease energy usage.
Security in Software Defined Networks (SDN): Challenges and Research Opportun...Editor IJCATR
In networks, the rapidly changing traffic patterns of search engines, Internet of Things (IoT) devices, Big Data and data centers has thrown up new challenges for legacy; existing networks; and prompted the need for a more intelligent and innovative way to dynamically manage traffic and allocate limited network resources. Software Defined Network (SDN) which decouples the control plane from the data plane through network vitalizations aims to address these challenges. This paper has explored the SDN architecture and its implementation with the OpenFlow protocol. It has also assessed some of its benefits over traditional network architectures, security concerns and how it can be addressed in future research and related works in emerging economies such as Nigeria.
Measure the Similarity of Complaint Document Using Cosine Similarity Based on...Editor IJCATR
Report handling on "LAPOR!" (Laporan, Aspirasi dan Pengaduan Online Rakyat) system depending on the system administrator who manually reads every incoming report [3]. Read manually can lead to errors in handling complaints [4] if the data flow is huge and grows rapidly, it needs at least three days to prepare a confirmation and it sensitive to inconsistencies [3]. In this study, the authors propose a model that can measure the identities of the Query (Incoming) with Document (Archive). The authors employed Class-Based Indexing term weighting scheme, and Cosine Similarities to analyse document similarities. CoSimTFIDF, CoSimTFICF and CoSimTFIDFICF values used in classification as feature for K-Nearest Neighbour (K-NN) classifier. The optimum result evaluation is pre-processing employ 75% of training data ratio and 25% of test data with CoSimTFIDF feature. It deliver a high accuracy 84%. The k = 5 value obtain high accuracy 84.12%
Hangul Recognition Using Support Vector MachineEditor IJCATR
The recognition of Hangul Image is more difficult compared with that of Latin. It could be recognized from the structural arrangement. Hangul is arranged from two dimensions while Latin is only from the left to the right. The current research creates a system to convert Hangul image into Latin text in order to use it as a learning material on reading Hangul. In general, image recognition system is divided into three steps. The first step is preprocessing, which includes binarization, segmentation through connected component-labeling method, and thinning with Zhang Suen to decrease some pattern information. The second is receiving the feature from every single image, whose identification process is done through chain code method. The third is recognizing the process using Support Vector Machine (SVM) with some kernels. It works through letter image and Hangul word recognition. It consists of 34 letters, each of which has 15 different patterns. The whole patterns are 510, divided into 3 data scenarios. The highest result achieved is 94,7% using SVM kernel polynomial and radial basis function. The level of recognition result is influenced by many trained data. Whilst the recognition process of Hangul word applies to the type 2 Hangul word with 6 different patterns. The difference of these patterns appears from the change of the font type. The chosen fonts for data training are such as Batang, Dotum, Gaeul, Gulim, Malgun Gothic. Arial Unicode MS is used to test the data. The lowest accuracy is achieved through the use of SVM kernel radial basis function, which is 69%. The same result, 72 %, is given by the SVM kernel linear and polynomial.
Application of 3D Printing in EducationEditor IJCATR
This paper provides a review of literature concerning the application of 3D printing in the education system. The review identifies that 3D Printing is being applied across the Educational levels [1] as well as in Libraries, Laboratories, and Distance education systems. The review also finds that 3D Printing is being used to teach both students and trainers about 3D Printing and to develop 3D Printing skills.
Survey on Energy-Efficient Routing Algorithms for Underwater Wireless Sensor ...Editor IJCATR
In underwater environment, for retrieval of information the routing mechanism is used. In routing mechanism there are three to four types of nodes are used, one is sink node which is deployed on the water surface and can collect the information, courier/super/AUV or dolphin powerful nodes are deployed in the middle of the water for forwarding the packets, ordinary nodes are also forwarder nodes which can be deployed from bottom to surface of the water and source nodes are deployed at the seabed which can extract the valuable information from the bottom of the sea. In underwater environment the battery power of the nodes is limited and that power can be enhanced through better selection of the routing algorithm. This paper focuses the energy-efficient routing algorithms for their routing mechanisms to prolong the battery power of the nodes. This paper also focuses the performance analysis of the energy-efficient algorithms under which we can examine the better performance of the route selection mechanism which can prolong the battery power of the node
Comparative analysis on Void Node Removal Routing algorithms for Underwater W...Editor IJCATR
The designing of routing algorithms faces many challenges in underwater environment like: propagation delay, acoustic channel behaviour, limited bandwidth, high bit error rate, limited battery power, underwater pressure, node mobility, localization 3D deployment, and underwater obstacles (voids). This paper focuses the underwater voids which affects the overall performance of the entire network. The majority of the researchers have used the better approaches for removal of voids through alternate path selection mechanism but still research needs improvement. This paper also focuses the architecture and its operation through merits and demerits of the existing algorithms. This research article further focuses the analytical method of the performance analysis of existing algorithms through which we found the better approach for removal of voids
Decay Property for Solutions to Plate Type Equations with Variable CoefficientsEditor IJCATR
In this paper we consider the initial value problem for a plate type equation with variable coefficients and memory in
1 n R n ), which is of regularity-loss property. By using spectrally resolution, we study the pointwise estimates in the spectral
space of the fundamental solution to the corresponding linear problem. Appealing to this pointwise estimates, we obtain the global
existence and the decay estimates of solutions to the semilinear problem by employing the fixed point theorem
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
When stars align: studies in data quality, knowledge graphs, and machine lear...
Occlusion and Abandoned Object Detection for Surveillance Applications
1. International Journal of Computer Applications Technology and Research
Volume 2– Issue 6, 708 - 713, 2013
Occlusion and Abandoned Object Detection for
Surveillance Applications
M. Chitra
RVS college of Engineering
and Technology
Karaikal, India
M.Kalaiselvi Geetha
Annamalai University
Chidambaram, India
L.Menaka
RVS college of Engineering
and Technology
Karaikal, India
Abstract: Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects
and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries
could potentially provide useful information about the scene’s structure and composition. A novel framework for blob based occluded
object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in
video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled
by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of
Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and
tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several
experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and
effectiveness of the proposed method.
Keyword: Occlusion, Histograms of Oriented Gradients descriptors, Support Vector Machine, mixture of Gaussians techniques, Blob,
abandoned object.
1. INTRODUCTION
Surveillance is monitoring the behavior, activities for the
purpose of influencing, managing, directing, or protecting.
One of the main advantages of video surveillance is that it
could be used both as a preventive mechanism and a forensic
tool for crimes. Unfortunately, constant surveillance of public
domains is challenging and labor-intensive. Therefore,
improved automated abandoned object detection and
occlusion detection systems are increasingly in demand.
(a). Occluded frame
(b). Abandoned object
Figure. 1 Occluded and abandoned frames
Robustness of object detection is affected by occlusion in the
presence of multiple objects. Occlusion is the hiding of an
object by another during multiple object tracking. In video
sequences Fig 1(a), occlusions create several challenges to the
tracking algorithm. The task of reliable detection and tracking
of multiple objects becomes highly complex for crowded
scenarios. The detection of suspicious object is one of the
most important task in video surveillance. Suspicious objects
are generally unattended packages left in public places. This
work aims to detect occlusions and abandoned object
detection which captures most of the researches working in
video surveillance. Object detection in monocular image
sequences still suffers from a lack of robustness due to
temporary occlusions, objects crossing and changing lighting
conditions.
Abandoned objects are detected by difference image. An
example for occluded scene and abandoned object/baggage is
shown in Fig. 1(b).
www.ijcat.com
The rest of the paper is arranged as follows: Section 2
discusses a few approaches explored by other researchers to
solve the problem. Section 3 explains the technical details of
our method, Section 4. Section 5 wraps up the paper with an
experimental result and conclusion of this paper.
2. RELATED WORKS
Although many algorithms have been proposed in the
literature, the problem of multiple interacting objects tracking
in complex scene is still open. Methods to solve the occlusion
problem in multiple interacting objects tracking have been
previously presented in [8, 12, 15, 16]. Sensor based
occlusion tracking methods [8, 18] cannot handle the
difference between moving and nonmoving obstacles. In
general, solutions to both pedestrian and vehicle occlusion
problems extend from spatial to temporal domains can be
broadly classified into many types. Some types like active
contour –based [2], stereo vision-based, region based, model
based [16], feature based [12] are also seen in the literature.
Object tracking in [17] overcome occlusion by fusing multiple
camera inputs, but it cannot handle complete occlusion.
708
2. International Journal of Computer Applications Technology and Research
Volume 2– Issue 6, 708 - 713, 2013
Several methods using color, texture and motion [13, 14, 19].
[14] Performs robust tracking that deals some instances of
occlusion but it cannot handle illumination changes.
Histograms of Oriented Gradients (HOG) and Local Binary
Pattern (LBP) [9] are used to detect partial occlusion but it
cannot handle the articulated deformation of people. Multicue model widely used in tracking and detection systems [14]
has achieved considerable success in object tracking because
of its simplicity and robustness. However, color features
cannot give good performance when an object and its
background have the similar colors. Contour-based object
detection and recognition method proposed in [2, 4]. [7,10]
uses filter based detection and tracking.
Most of the proposed techniques for abandoned object
detection rely on tracking information [24, 25] to detect dropoff events, while fusing information from multiple cameras.
As stated by Porikli [26], these methods are not well suited to
complex environments like scenes involving crowds and large
amounts of occlusion. Stauffer and Grimson [27] present an
event detection module that classifies objects, including
abandoned objects, using a neural network, but is limited to
detecting only one abandoned object at a time. In [28] a multicamera video surveillance system is proposed to detect the
owner of each abandoned object is determined and tracked
using distance and time constraints and multiple cameras with
overlapping fields of view are exploited to cope with
occlusion of various types.
3. OCCLUSION AND ABANDONED
OBJECT DETECTION
3.1 Occlusion Detection
Manual surveillance requires the system to be monitored by
personnel and is expensive and inconvenient. Hence, efforts
have been put into automatic surveillance. One of the main
components of automatic video surveillance is detecting
occluded objects. Fig.2 shows the proposed system
architecture for occlusion detection.
Occlusion is the main cause for performance degradation in
surveillance systems. Under occlusion, the objects will
become overlapped and may be found moving together in a
scene. Occlusion can be of three types. Self occlusion, interobject occlusion, background occlusion. The goal of our work
is to develop a general framework to detect and track objects
with persistent and occasional heavy occlusion. Background
subtraction is carried out by a mixture of Gaussians
techniques (MOG) is in Fig.3. Blob detection for detecting
occluded object is carried out in OpenCV. Multiple occluded
object detection involves computing the Histogram of
Oriented Gradients descriptors (HOG) and linear Support
Vector Machine (SVM) as a classifier.
Figure. 2 Occlusion detection
Figure. 3 Mixture of Gaussians algorithms
Model the values of a particular pixel as a mixture of
Gaussians. We determine which Gaussians may correspond
to background colors-Based on the persistence and the
variance of each of the Gaussians [23]. Pixel values that do
not fit the background distributions are considered foreground
until there is a Gaussian that includes them. Update the
Gaussians. Pixel values that do not match one of the pixel's
―background‖ Gaussians are grouped using connected
components.
Background modeling - constructs a reference image
representing the background.
Threshold selection- determines appropriate threshold values
used in the subtraction operation to obtain a desired detection
rate.
Pixel classification - classifies the type of a given pixel, i.e.,
the pixel is the part of background (including ordinary
background and shaded background), or it is a moving object.
3.1.1 Mixture of Gaussian Algorithm(MOG)
Pixelprocesses–
{X1,…….,XT} = {I(x0, y0, i): 1≤ i ≤ t},
Where, I- image sequence, t- time, {x0,y0}- the history of
pixels.
Figure. 4 Background subtraction using MOG
www.ijcat.com
709
3. International Journal of Computer Applications Technology and Research
Volume 2– Issue 6, 708 - 713, 2013
3.1.2 Blob Detection
Once the background subtraction is done with MOG, each
foreground object is detected as a blob. Each module is
applied on a bi-level image obtained though a mixture of
Gaussian background subtraction and some basic filtering
performed. Using OpenCV [22]. A tracked blob is considered
to be occluded if its major region is covered by foreground
and it should continue to be tracked if either it is occluded or
its area and centroid is matched with any of the blobs. When
two objects pass close to each other, they are detected as a
single blob. Fig.5 shows the blob detection.
Often, one object will become occluded by the other one. One
of the challenging problems is to maintain correct labeling of
each object after they split again. Fig.6 demonstrates the blob
merging and splitting at the time of occlusion.
of each gradient sample rotated relative to key point
orientation.
Image gradients
Key point descriptor
Figure. 7 Histograms of Oriented Gradients
For normalization process all blocks are typically
overlapped and rotation invariance. It is typically combined
with SVM classifier.
3.1.4 SVM Classifier
Figure. 5 Blob Detection and Tracking
The Support Vector Machine classifier is a binary classifier
which looks for an optimal hyper plane as a decision function.
Once trained on images containing some particular object, the
SVM [20] classifier can make decisions regarding pedestrians.
SVM is based on the principle of structural risk minimization.
For linearly separable data, SVM finds the separating hyper
plane which separates the data within the largest margin. For,
linearly inseparable data, it maps the data in the input space
into high dimension feature space x € Iı—>Φ(x) € H With
kernel function Φ(x) , to find the separating hyper plane.
SVM was originally developed for two class classification
problems. The N class classification problem can be solved
using N SVMs. Each SVM separates a single class from all
the remaining classes (One-vs.-rest approach).Given a set of
frames corresponding to N classed for training, N SVMs are
trained. Each SVM is trained to distinguish a class and other
classes in the training set. During testing, the class label y of a
class x can be determined using:
y=
Where, dn (x) = max{di (x)}N i=1, and di(x) is the distance
from x to the SVM hyper plane corresponding to frame i,
the Classification threshold is t, and the class label y=0 stands
for unknown.
Figure. 6 Blob Merging and splitting at the time of occlusion
3.1.3 Histogram of Oriented Gradients descriptors
(HOG)
Histograms of Oriented Gradients (HOG) are feature
descriptors used in computer vision and image processing for
the purpose of object detection. The technique counts
occurrences of gradient orientation in localized portions of an
image. This method is similar to that of edge orientation
histograms, but differs in that it is computed on a dense grid
of uniformly spaced cells and uses overlapping local contrast
normalization for improved accuracy. Fig.7 shows orientation
www.ijcat.com
Figure. 8 Blob Optimal separating hyper plane and margin for a two
dimensional feature space
710
4. International Journal of Computer Applications Technology and Research
Volume 2– Issue 6, 708 - 713, 2013
3.2 Abandoned Object Detection
4. EXPERIMENTAL RESULTS
The detection of suspicious (dangerous) object is one of the
most important task in video surveillance. An abandoned
object not belonging to the background scene and remaining
in the same position for a long time. Our system for
abandoned object detection was designed to assist operators
surveilling indoor environments, such as airports, railway, or
metro stations etc. Fig 9 shows the abandoned object
detection.
Experiments were carried out on a PC with Intel IV cores
2.67GHz processor with C++ and the OpenCV library. The
above approaches are implemented and experiments are
performed on video clips freely available and PETS 2006 .The
detection rate of proposed methods are shown in Table 1. The
video data are processed at 20fps, and 320x240 resolution.
We present some snapshots for occlusion detection and
abandoned object detection in Fig.13, 14.
4.1 Occlusion detection
4.1.1 Occlusion degree and detection rate
The occlusion degree is calculated by the number of moving
pedestrians and moving vehicles present in the frame with
occlusion. For example if a video sequence contains 10
pedestrians, in which 5 pedestrians are occluded with each
other, then the calculated occlusion degree is 50 %. More than
10 persons is considered as high, more than 5 as medium less
than 5 as low occlusion degree. The occlusion rate is
compared with the ground truth information.Fig.11 represents
the graphical representation of occlusion detection.
4.1.2 Ground truth method
The ground-truth data allows us to do a quantitative
comparison between occlusion detection method and
the facts that are confirmed in an actual field. The ground
truth method calculation is carried out manually.
Table 1. Detection rate of proposed method
Figure. 9 Abandoned Object Detection
Occlusion degree (%)
Detection rate
Ground truth method
High
80
90
medium
90.3
95
low
91.2
100
3.2.1 Difference Image
The difference image is defined as the residual image that
results from subtracting the scenes at two consecutive time
instants from each other. Difference images capture target
motion in the scene and can be used for target tracking. Fig.
10 illustrates the basic idea behind difference images. Fig.
10(a), 10(b) show a scene with moving targets at two
consecutive time instants. Fig. 10(c) shows the difference
image resulting from subtracting the two image frames.
Figure. 11 Graphical reprsentation of our detection rate.
4.2 Abandoned Object Detection
4.2.1 Intensity Calculation of Difference Image
(a)
(b)
(c)
The intensity of the difference images are taken as an input to
the SVM classifier.
Fig. 12 shows the graphical
representation of difference images intensity values of 346
difference images from the abandoned object dataset.
Figure. 10 (a). Image x1 at time instance t1 (b). Image x2 at time
instance t2, and (c). Difference image.
www.ijcat.com
711
5. International Journal of Computer Applications Technology and Research
Volume 2– Issue 6, 708 - 713, 2013
Table 2. Data set for abandoned object detection
S.NO
Dataset or video name
Video length
1
Abandoned object
59
Figure. 13 (b). Occlusion detetion in frame 1 at video 10
Figure. 13 Occlusion detection in random frame number.
Video 1
3
30
PETS 2006
2
60
(a) Frame differencing done with first and 4th frame
(b) Frame differencing done with first and 12th frame
Figure. 12 Intensity values of the difference image in abandoned
object detection
4.2.2 Results with SVM Classifier
The Support Vector Machine classifier is a binary classifier
which looks for an optimal hyper plane as a decision function.
Once trained on images containing some particular object, the
SVM classifier can make decisions regarding the presence of
an object, such as a human being. Classification results using
quadratic discriminant analysis were found to be quite
accurate. A training set of 60 cases, where 30 of them belong
to one class - bag, and the rest belong to the second class non-bag(people) is used. Using a test set of 50 cases, gave us
an accuracy of 91%. Fig. 28 shows the SVM training and
testing.
(c)Frame differencing done with first and 18th frame
(d) Frame differencing done with first and 26th frame.
Figure. 14 Abandoned object detection using frame differencing
at frame 4, 12, 18, 26.
5. CONCLUSION
Figure. 13 (a). Occlusion detetion in frame 1 at video 4
www.ijcat.com
This research work will lay a stepping stone for the further
developments of the automatic object tracking system in a
secured area. A real-time multiple occluded objects detection
and abandoned object detection system is presented.
Experiments on complex indoor and outdoor environments
show that the system can deal with difficult situations such as
ghosts and illumination changes. Moreover, it can track
multiple objects with long-duration and complete occlusion.
While the system is highly computationally cost effective and
712
6. International Journal of Computer Applications Technology and Research
Volume 2– Issue 6, 708 - 713, 2013
accurate. The testing results which are based on different
scenarios have proved that our approach can be successfully
applied in real world surveillance applications. Future work
includes extending multiple camera view occlusion detection
and abandoned object detection.
6. REFERENCES
[1] Pallavi S. Bangare, Nilesh J. Uke, Sunil L. Bangare, ― An
Approach for Detecting Abandoned Object from Real Time
Video‖, International Journal of Engineering Research and
Applications (IJERA), Vol. 2, Issue 3, 2012.
[2] Geo Guo,Ting Tiang, Yizhou wang and Wen Gao,
―Recovering missing Contours for occluded Object
detection‖, IEEE Signal processing letters, Vol.19,No.8, Aug
2012.
[3] A.A.shafie,M.H Ali,Fadhlan Hafiz and Rosilizer M.Ali,
―Smart video Surveillance System for Vehicle Detection and
traffic Flow control‖, Journal of Engineering Science and
technology, Vol. 6, No. 4, 2011.
[4] Tianyang Ma and Longin Jan Latecki, ―From Partial
Shape Matching through Local Deformation to Robust Global
Shape Similarity for Object Detection‖, CPVR 2011.
[5] Manmohan Singh Rawat, Varun N.S, Vinotha S.R,
―Occlusion Detection and Handling In Video Surveillance‖,
2011 International Conference on Future Information
Technology, IPCSIT vol. 13 2011.
[6] Kung –Chung Lee,Anand Oka, Emmanual Pollakis and
Lutz Lampe, ― A Comparison between unscented Kalman
filtering and Particle filtering for RSII-BasedTracking,‖
Positioning Navigation and Communication (WPNC), 2010.
[7] Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier,
E., Van Gool,L., ‖ Robust tracking-by-detection using a
detector confidence particle filter‖,
IEEE International
Conference on Computer Vision ,2009.
[14] Valtteri Takala and Matti Pietik¨ainen, ―Multi-Object
Tracking Using Color, Texture and Motion‖, IEEE Trans on.
Image Processing, 2007.
[15] Tao Yang, Stan Z.Li, Quan Pan, Jing Li, ―Real-time
Multiple Objects Tracking with Occlusion Handling in
Dynamic Scenes‖, Foundation of National Laboratory of
Pattern Recognition and National Natural Science Foundation
of China (60172037) , 2005.
[16] Clement Chun Cheong Pang, William Wai Leung Lam,
Nelson Han Ching Yung, ―A Novel method for Resolving
vehicle occlusion in a monocular Traffic-Images Sequences‖,
IEEE Transaction on Intelligent Transportation systems, Vol.
5, no. 3, sep 2004.
[17] Tao Zhao, Ram Nevatia ―Tracking Multiple Humans in
Crowded Environment‖, IEEE Computer Society Conference
on Computer Vision and Pattern Recognition (CVPR’04)
[18] MichaelBramberger, RomanPflugfelder, BernhardRinner,
Helmet Schwabach and Bernhard Strobl, ―Intelligent Traffic
Video Sensor : Architecture and Applications‖, IEEE 2003.
[19] P.Perez, C.Hue, J.Vermaak, and M.Gangnet ―ColorBased Probabilistic Tracking‖, A. Heyden et al. (Eds.): ECCV
2002, LNCS 2350, pp. 661–675, 2002.
[20] Navneet Dalal and Bill Triggs, ―Histograms of Oriented
Gradients for Human Detection‖,IEEE Computer Society
Conference on Computer Vision and Pattern Recognition,
2005.
[21] Asbjørn Berge, ―INF 5300 Video Analysis Detecting
motion‖, SINTEF.
[22] http://mateuszstankiewicz.eu/?p=189
[23] P. KaewTraKulPong, R. Bowden, ―An Improved
Adaptive Background Mixture Model for Realtime Tracking
with Shadow Detection‖, In Proc. 2nd European Workshop on
Advanced Video Based Surveillance Systems, AVBS01. Sept
2001.
[8] Michael S. Darms, Paul E. Rybski, Christopher Baker, and
Chris Urmson, ―Obstacle Detection and Tracking for the
Urban Challenge‖, IEEE Transaction on Intelligent
Transportation systems,Vol. 10, No. 3, Sep 2009.
[24] S. Guler, J. A. Silverstein, and I. H. Pushee, ―Stationary
Objects in Multiple Object Tracking‖, IEEE International
Conference on Advanced Video and Signal-Based
Surveillance, London , UK , Sep 2007.
[9] Xiaoyu Wang, Tony X. Han, Shuicheng Yan, ―An HOGLBP Human Detector with Partial Occlusion Handling‖,
Computer Vision, IEEE 12th International Conference, 2009.
[25] J. M. del Rincn, J. E. Herrero-Jaraba, J. R. Gomez, and
C. Orrite Urunuela, ―Automatic left luggage detection and
tracking using multi cameras,‖ in PETS, 2006, pp. 59–66.
[10] Junliang Xing, Haizhou Ai, Shihong Lao,―Multi-object
tracking through occlusions by localtracklets filtering
and global tracklets association with detection responses‖,
Computer Vision and Pattern Recognition, 2009. CVPR
2009.
[26] F. Porikli, Y. Ivanov, and T. Haga, Robust Abandoned
Object detection Using Dual Foregrounds, EURASIP Journal
on Advances in Signal Processing, Vol. 2008, Hindawi
Publishing Corporation.
[11]
Dollar, P., Wojek, C. ., Schiele, B. , Perona,
P. ,―Pedestrian detection: A benchmark‖, Computer Vision
and Pattern Recognition, . CVPR 2009.
[27] Stauffer, C., Grimson, W.E.L.: Learning patterns of
activity using real-time tracking. IEEE Trans. Pattern Anal.
Mach. Intell. (PAMI) 22(8), 747–757 2000
[12] Pang, C.C.C. ; Lam, W.W.L. ; Yung, N.H.C. , ―A
Method for Vehicle Count in the Presence of MultipleVehicle Occlusions in Traffic Images‖, IEEE Transactions on
Intelligent Transportation Systems, Sept. 2007.
[13] Junqiu WANG and Yasushi YAGI, ―Integrating Color
and Shape-Texture Features for Adaptive Real-time Object
Tracking‖, IEEE Trans on. Image Processing, 2007.
www.ijcat.com
[28] M.D.Beynon, D.J.Van Hook, M.Seibert, A.Peacock,
D.Dudgeon ―Detecting Abandoned Packages in a Multicamera Video Surveillance System‖ IEEE Conference on
Advanced Video and Signal Based Surveillance (AVSS'03)
July 21 - 22, 2003 Miami, Florida P. 221.
713