Social Attribute-Aware Force Model: Exploiting Richness of Interaction for Abnormal Crowd Detection

Interactions among pedestrians usually play an important role in understanding crowd behavior. However, there are great challenges, such as occlusions, motion, and appearance variance, on accurate analysis of pedestrian interactions. In this paper, we introduce a novel social attribute-aware force model (SAFM) for detection of abnormal crowd events. The proposed model incorporates social characteristics of crowd behaviors to improve the description of interactive behaviors. To this end, we first efficiently estimate the scene scale in an unsupervised manner. Then, we introduce the concepts of social disorder and congestion attributes to characterize the interaction of social behaviors, and construct our crowd interaction model on the basis of social force by an online fusion strategy. These attributes encode social interaction characteristics and offer robustness against motion pattern variance. Abnormal event detection is finally performed based on the proposed SAFM. In addition, the attribute-aware interaction force indicates the possible locations of anomalous interactions. We validate our method on the publicly available data sets for abnormal detection, and the experimental results show promising performance compared with alternative and state-of-the-art methods.