Particle-based tracking model for automatic anomaly detection

In this paper, we present a new method to automatically discover recurrent activities occurring in a video scene, and to identify the temporal relations between these activities, e.g. to discover the different flows of cars at a road intersection, and to identify the traffic light sequence that governs these flows. The proposed method is based on particle-based trajectories, analyzed through a cascade of HMM and HDP-HMM models. We demonstrate the effectiveness of our model for scene activity recognition task on a road intersection dataset. We last show that our model is also able to perform on the fly abnormal events detection (by identifying activities or relations that do not fit in the usual/discovered ones), with encouraging performances.