Anomaly detection in crowds assisted by scene perspective projection correction

In this paper, we propose a novel approach based on compensating for the perspective projection effect for anomaly detection in crowds. Video frames obtained by a camera have a common rule of perspective projection effect. The law of perspective projection makes anomaly detection a challenge task because of no consistency in each video frame. For the sake of overcoming the drawback caused by perspective projection, we innovatively design an approach based on compensating for images under perspective projection to eliminate the influence of perspective projection. Then a space Markov Random Field (MRF) is modeled to build normal behavior patterns considering both single node behavior and the correlation of adjacent nodes. An energy function is formulated as the evaluation criterion to detect anomaly. Experiments prove that our approach can detect abnormal events effectively and robustly.