Social MIL: Interaction-Aware for Crowd Anomaly Detection

Abstract

Crowd anomaly detection under surveillance scene is a quite challenging task, which often companies with not rare objects, unexpected bursts in activity and complex dynamic patterns. In this paper, we propose a social multiple-instance learning(MIL) framework with a dual-branch network by considering dynamic interaction among groups, individuals and environment to obtain attentive spatial-temporal feature representation. First, MIL is employed to overcome the challenge of rare training abnormal samples and video-based labels. The social force map is utilized for modeling behavior interaction to supply the prior knowledge. In addition, we introduce the self-attention module, which represents a more discriminative spatial-temporal feature based on C3D network through implementing weight redistribution inside the feature. The results of the experiments conducted on UCF-Crime dataset show that the proposed dual-branch social multiple-instance learning (MIL) anomaly detection framework with the dual-branch network outperforms than existing approaches and obtains the state-of-the-art performance.