Predictive multiple motion fields for trajectory completion: Application to surveillance systems

Extraction of trajectories of moving objects from video is an important step towards activity classification in a surveillance system. It has been recently shown that multiple motion fields (MMF) estimated from trajectories can efficiently describe the movement of objects, and allow an automatic classification of activities in the scene. However, the object segmentation and tracking may introduce gaps in trajectories due to the sensor failures (i.e. miss detections) or pedestrian occlusions. This may hamper the performance of activity classification from the observed trajectories. In this paper, we propose a predictive motion model based on MMF to describe the trajectory of the object. Precomputed motion fields are used to predict the possible position of an object based on its location and trajectory up to that point and therefore, the method can deal with the case of a missed detection in one or more frames, thus able to perform trajectory completion. Experiments on real data show that the proposed method is remarkable in dealing with up to around 70% of the positions missing in the pedestrian trajectories.