Trajectory rectification and path modeling for video surveillance

Path modeling for video surveillance is an active area of research. We address the issue of Euclidean path mod-eling in a single camera for activity monitoring in a multi-camera video surveillance system. The paper proposes (i) a novel linear solution to auto-calibrate any camera observ-ing pedestrians and (ii) to use these calibrated cameras to detect unusual object behavior. During the unsupervised training phase, after auto-calibrating a camera and metric rectifying the input trajectories, the input sequences are reg-istered to the satellite imagery and prototype path models are constructed. This allows us to estimate metric informa-tion directly from the video sequences. During the testing phase, using our simple yet efficient similarity measures, we seek a relation between the input trajectories derived from a sequence and the prototype path models. We test the proposed method on synthetic as well as on real-world pedestrian sequences. 1