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 walk-ing motion. Unlike existing techniques we do not assume clean point tracks but instead propose “probabilistic tra-jectories ” as new features to classify. These are extracted from directed acyclic graphs whose edges represent tem-poral point correspondences and are weighted with their matching probability in terms of appearance and location. This representation tolerates the inherent trajectory ambi-guity, for example due to occlusions. We then learn the correlation between the movement of two feet using a ran-dom forest classifier. The effectiveness of the algorithm is demonstrated in experiments on image sequences captured with a static camera. 1