Abnormal crowd behavior detection based on social attribute-aware force model

Yanhao Zhang, Lei Qin, Hongxun Yao, Qingming Huang

Abstract: In this paper, a novel social attribute-aware force model is presented for abnormal crowd pattern detection in video sequences. We take social characteristics of crowd behaviors into account in order to improve the effectiveness of the simulation on the interaction behaviors of the crowd. A quick unsupervised method is proposed to estimate the scene scale. Both the social disorder attribute and congestion attribute are introduced to describe the realistic social behaviors by using statistical context feature. Through the semantic attribute-aware enhancement, we obtain an improved model on the basis of social force. We validate our method in public available datasets for abnormal detection, and the experimental results show promising performance compared with other state of the art methods.