B4 Object Detection And Tracking In Intelligent Video Surveillance

Intelligent video surveillance system is based on digital, networked surveillance, which can extract and analyze the key information from video source, timely detect and deal with the unusual cases on the monitored scene, thus to be able to effectively assist the security personnel dealing with the crisis and minimizing false negative and false positive by using image processing and pattern recognition techniques. When the building of a HharmoniousH HsocietyH is emphasized in our country and"building safe city, and promoting social harmony"is becoming a society-wide goal, the intelligent video surveillance technology has broad application prospect and great potential economic value both in scientific research and engineering applications.As a critical part of intelligent video surveillance system, detection and tracking of moving targets is becoming a hotspot in computer vision community. However, before it get an extensive and reliable applications, several problems have to be resolved such as object detection in complex scenario, tracking in occlusion condition and real time tracking. On the basis of the analysis of dominant algorithms for target detection and tracking, we propose several techniques for solving the first two problems. The main contributions of this thesis are as follows:Algorithms for object detection are studied. Some classical algorithms for moving target detection, including HframeH differential method, background subtraction method, optical flow method are implemented, their strengths and weaknesses are discussed; After currently widely used Gaussian background modeling is discussed in detail, an improved method for Gaussian background modeling was proposed. The experiment results show that our improved method can extract effectively the moving objects and fit for real time detection.Algorithms for object tracking are studied. Mean shift algorithm and Kalman filter is applied in object tracking. Considering each of them has its own disadvantages and they have complementary advantages, we present a new algorithm combining the Kalman prediction with mean shift. By using Kalman filter to predict locations where moving objects most probably appear in the next frame and mean shift to search in the corresponding areas and match the moving objects, the approach promises to obtain more reliable tracking effect with much less computation cost. We determine whether the tracking target is occluded by similarity function in Mean Shift. When the occlusion happens, we update filter by using directly predicting points rather than the convergence point information of mean shift. The experiment results show that our algorithm can track the moving target well and also has better robustness under occlusion.