An unsupervised abnormal crowd behavior detection algorithm

Abstract

In this paper, we propose a detection algorithm based on people counting for two special kinds of abnormal crowd behavior, gathering and dispersing. We use an efficient foreground segmentation algorithm for calculating the number of people, which uses an approximate median filter and double background model to obtain a reliable foreground. Further, counting people globally based on potential energy model in crowd scenes. In order to detecting unnormal crowd behavior happened, a crowd distribution curve is proposed, which combines results of counting and crowd entropy to evaluate the spatial distribution of throng, and describes the global distribution as a good feature. Experiments prove that our proposed method is able to detect the abnormal crowd behavior efficiently without camera calibration or supervised training.