Correlated probabilistic trajectories for pedestrian motion detection

This paper introduces an algorithm for detecting walking motion using point trajectories in video sequences. Given a number of point trajectories, we identify those which are spatio-temporally correlated as arising from feet in walking motion. Unlike existing techniques we do not assume clean point tracks but instead propose "probabilistic trajectories" as new features to classify. These are extracted from directed acyclic graphs whose edges represent temporal point correspondences and are weighted with their matching probability in terms of appearance and location. This representation tolerates the inherent trajectory ambiguity, for example due to occlusions. We then learn the correlation between the movement of two feet using a random forest classifier. The effectiveness of the algorithm is demonstrated in experiments on image sequences captured with a static camera.