Anomalous crowd behavior detection and localization in video surveillance

In this paper, we focus on the problem of detection and localization of crowd escape anomalous behaviors in video surveillance systems. The scheme proposed can not only detect the abnormal events which have been studied, but also detect the possible location of abnormal events. People usually instinctively escape from a place where abnormal or dangerous events occur. Based on this inference, a novel algorithm of detecting the divergent center is proposed: The divergent center indicates possible place where abnormal events occur. The model of crowd motion in both the normal and abnormal situations has been made according to the proposed method. Intersections of vector are obtained through solving the straight line equation sets, where the straight line Equation sets are determined by the location and direction of motion vector which are calculated by the optical flow. Then the dense regions of intersection sets, i.e., the divergent center, are obtained by using the distance segmentation method, the threshold method and the graphical method. Escape detection is finally judged according to the speed and energy of motion and the divergent center. Experiments on UMN datasets and other real videos show that the proposed method is valid on crowd escape behavior detection.