Real-time detection of abnormal crowd behavior using a matrix approximation-based approach

Automatic detection of abnormal crowd activities is one of central tasks in video surveillance. In this paper we present a matrix approximation-based method to detect abnormal crowd behavior. In our approach, we model typical motions associated with normal crowd behaviors with a set of motion subspaces, computed through low-rank matrix approximation. Then, abnormal crowd behaviors are identified by the motion deviations from the representative subspaces. Our method does not require complicated tracking or classification method, and can fast detect abnormal events in complex crowd scenes. In addition, through the adaptive learning module, our model is built on the observed data, and can be expanded by incorporating new crowd behavior patterns during the detection process. The results on simulated crowd scenes show the effectiveness of our method.