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Anomaly detection and localization in crowded scenes using short-term trajectories

In this paper we present a method to detect and localize abnormal events in crowded scene. Most existing methods use the patch of optical flow or human tracking based trajectory as representation for crowd motion, which inevitably suffer from noises. Instead, we propose the employment of a new and efficient feature, short-term trajectory, which represent the motion of the visible and constant part of human body that move consistently, for modeling the complicated crowded scene. To extract the short-term trajectory, 3D mean-shift is firstly used to smooth the video frames and 3D seed filling algorithm is performed. In order to detect the abnormal events, all short-term trajectories are treated as point set and mapped into the image plane to obtain probability distribution of normalcy for every pixel. A cumulative energy is calculated based on these probability distributions to identify and localize the abnormal event. Experiments are conducted on known crowd data sets, and the results show that our method can achieve high accuracy in anomaly detection as well as effectiveness in anomalies localization.