A Real-Time System for Abnormal Path Detection

This paper proposes a real-time system capable to extract andmodel object trajectories from a multi-camera setup with theaim of identifying abnormal paths. The trajectories are modeledas a sequence of positional distributions (2D Gaussians)and clustered in the training phase by exploiting an innovativedistance measure based on a global alignment techniqueand Bhattacharyya distance between Gaussians. An on-lineclassification procedure is proposed in order to on-the-fly classifynew trajectories into either “normal” or “abnormal” (in thesense of rarely seen before, thus unusual and potentially interesting).Experiments on a real scenario will be presented