Crowd Behavior Representation Using Motion Influence Matrix for Anomaly Detection

In this paper, we propose a new method to detect abnormal behavior in crowd video. The motion influence matrix is proposed to represent crowd behaviors. It is generated based on concept of human perception with block-level motion vectors which describe actual crowd movement. Furthermore, a generalized framework is developed to detect abnormal crowd behavior using motion influence matrix. The proposed method has an advantage of that does not require any human detection or segmentation method which make it robust to human detection error by using optical flows which is extracted from two continuous frames. In this model, a normal behavior is presented by a low motion influence value. On the other hand, a high motion influence value indicates occurrence of abnormal behavior. Spatio-temporal cuboids are extracted from the motion influence matrix to measure the unusualness of the frame. Two different kinds of abnormal behaviors are dealt in this research: global abnormal behavior and local abnormal behavior. For the quantitative measurement of effectiveness of the proposed method, we evaluate our algorithm on two datasets: UMN and UCSD for global and local abnormal behavior, respectively. Experimental results show that the proposed method outperforms the competing methods.