Object Detection And Behavior Analysis In Video Surveillance

Video surveillance refers to the use of computer vision and video analysis of video camera recording the image sequence to automatically analyze and determine their behavior without the need for human intervention, thereby to achieve the completion of day-to-day management and alert to the abnormal situation. The subject is committed to developing real-time monitoring of the behavior identification system. The system is based on moving object detection and tracking module and it can definite human behavior and process behavior identification. For example, when there are"perimeter intrusion","anti-violation of exhibition area"and other illegal behavior, the system can automatically identify and conduct real-time alarm, crawl the time of the incident image and position violation objectives. In addition, in order to further develop a more universal human detection algorithm and behavior analysis system, we study the human detection and 3D human pose estimation. The following is the work done in this article:(1)This work conducts a comparative study of a wide range of background modeling method. Because video surveillance system involves of dynamic scenes, we proposed a general foreground detection framework which adopts mixed-Gaussian methods and information fusion background modeling method. The framework can automatically remove noise.(2) One of the main difficulties of the tracking process concerns the partial or total temporal occlusions of the objects. Therefore, we establish a tracking strategy to divide tracking process into seven different events to handle with. Objects can be divided and merged in tracking process which gives a high requirement for object matching. One character matching method based on color histogram similarity has been used to make objects matching in image sequence. In order to recognize behavior, we defined several behaviors and prove its effectiveness through experiments.(3)Besides, we address the problem of 3D human pose estimation in a single real scene image. Normally, 3D pose estimation from real image needs background subtraction to extract the appropriate features. We don't make such assumption. In this paper, a two-step approach is proposed, first, instead of applying background subtraction to get the segmentation of human, we combine the segmentation with human detection using an ISM-based detector. Then, silhouette feature can be extracted and 3D pose estimation is solved as a regression problem. RVMs and ridge regression method are applied to solve this problem. The results show the robustness and accuracy of our method.