Crowd Escape Behavior Detection and Localization Based on Divergent Centers

Abstract

In this paper, we propose a novel framework for anomalous crowd behavior detection and localization by introducing divergent centers in intelligent video surveillance systems. In this paper, the scheme proposed can deal with this problem by modeling the crowd motion obtained from the optical flow. The obtained magnitude, position and direction are used to construct the motion model. The method of the weighted velocity is applied to calculate the motion velocity. People usually instinctively escape from a place where abnormal or dangerous events occur. Based on this inference, a novel algorithm of detecting divergent centers is proposed: divergent centers indicate possible places where abnormal events occur. The proposed algorithm of detect divergent centers can identify more than one divergent center by analyzing the intersections of vectors, and this algorithm consist of the distance segmentation method and the nearest neighbor search. The performance of our method is validated in a number of experiments on public data sets.