A real-time system for abnormal path detection
Simone Calderara, C. Alaimo, Andrea Prati, Rita Cucchiara · 2009
This paper proposes a real-time system capable to extract and model object trajectories from a multi-camera setup with the aim of identifying paths. The trajectories are modeled as a sequence of positional distributions (2D Gaussians) and clustered in the training phase by exploiting an innovative distance measure based on a global alignment technique and Bhattacharyya distance between Gaussians. An on-line classification procedure is proposed in order to on-the-fly classify new trajectories into either normal or abnormal (in the sense of rarely seen before, thus unusual and potentially interesting). Experiments on a real scenario will be presented. (6 pages)