Moving Object Detection and Tracking for Video Surveillance: A Review

This paper presents a review and systematic study on the moving object detection and surveillance of the video as it is an important and challenging task in many computer vision applications, such as human detection, vehicles detection, threat, and security. Video surveillance is a dynamic environment, especially for human and vehicles and for specific object in case of security is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism, crime, public safety and for efficient management of accidents and crime scene going on now days. The paper also presents the concept of real time implementation computing task in video surveillances system. In this review paper various methods are discussed were evaluation of order to access how well they can detect moving object in an outdoor/indoor section in real time situation

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Detecting human heads with their orientations

We propose a two-step method for detecting human heads with their orientations. In the first step, the method employs an ellipse as the contour model of human-head appearances to deal with wide variety of appearances. Our method then evaluates the ellipse to detect possible human heads. In the second step, on the other hand, our method focuses on features inside the ellipse, such as eyes, the mouth or cheeks, to model facial components. The method evaluates not only such components themselves but also their geometric configuration to eliminate false positives in the first step and, at the same time, to estimate face orientations. Our intensive experiments show that our method can correctly and stably detect human heads with their orientations

Face tracking system based on color, stereovision and elliptical shape features

Abstract In this paper we present a vision system that performs tracking a human face in 3D

A Study on Human Motion Acquisition and Recognition Employing Structured Motion Database

九州工業大学博士学位論文 学位記番号:工博甲第332号　学位授与年月日:平成24年3月23日1 Introduction||2 Human Motion Representation||3 Human Motion Recognition||4 Automatic Human Motion Acquisition||5 Human Motion Recognition Employing Structured Motion Database||6 Analysis on the Constraints in Human Motion Recognition||7 Multiple Persons’ Action Recognition||8 Discussion and ConclusionsHuman motion analysis is an emerging research field for the video-based applications capable of acquiring and recognizing human motions or actions. The automaticity of such a system with these capabilities has vital importance in real-life scenarios. With the increasing number of applications, the demand for a human motion acquisition system is gaining importance day-by-day. We develop such kind of acquisition system based on body-parts modeling strategy. The system is able to acquire the motion by positioning body joints and interpreting those joints by the inter-parts inclination. Besides the development of the acquisition system, there is increasing need for a reliable human motion recognition system in recent years. There are a number of researches on motion recognition is performed in last two decades. At the same time, an enormous amount of bulk motion datasets are becoming available. Therefore, it becomes an indispensable task to develop a motion database that can deal with large variability of motions efficiently. We have developed such a system based on the structured motion database concept. In order to gain a perspective on this issue, we have analyzed various aspects of the motion database with a view to establishing a standard recognition scheme. The conventional structured database is subjected to improvement by considering three aspects: directional organization, nearest neighbor searching problem resolution, and prior direction estimation. In order to investigate and analyze comprehensively the effect of those aspects on motion recognition, we have adopted two forms of motion representation, eigenspace-based motion compression, and B-Tree structured database. Moreover, we have also analyzed the two important constraints in motion recognition: missing information and clutter outdoor motions. Two separate systems based on these constraints are also developed that shows the suitable adoption of the constraints. However, several people occupy a scene in practical cases. We have proposed a detection-tracking-recognition integrated action recognition system to deal with multiple people case. The system shows decent performance in outdoor scenarios. The experimental results empirically illustrate the suitability and compatibility of various factors of the motion recognition

To Appear in ACCV-98, Mumbai-India, Material Subject to ACCV Copy-Rights Visual Surveillance of Human Activity

this paper we provide an overview of recent research conducted at the University of Maryland's Computer Vision Laboratory on problems related to surveillance of human activities. Our research is motivated by considerations of a ground-based mobile surveillance system that monitors an extended area for human activity. During motion, the surveillance system must detect other moving objects and identify them as humans, animals, vehicles. When one or more persons are detected, their movements need to be analyzed to recognize the activities that they are involved in. Ideally, the surveillance system would be able to accomplish this even while continuing to move; alternatively, the system could stop and stare at that part of the scene containing peopl