Human Tracking Using a Top-Down and Knowledge Based Approach

In this paper, we propose a new top-down and knowledge-based approach to perform human tracking in video sequences. First, introduction of knowledge allows to anticipate most of common problems encountered by tracking methods. Second, we define a top-down approach rather than a classical bottom-up approach to encode the knowledge. The more global point of view of the scene provided by our top-down approach also allows to keep some consistency among the set of trajectories extracted from the video sequence. A preliminary experimentation has been conducted over some challenging sequences of the PETS 2009 dataset. The obtained results confirm that our approach can still achieve promising performance even with a consistent reduction in the amount of information taken into account during the tracking process. In order to show the relevance of considering knowledge to address tracking problem, we strongly reduce the amount of information provided to our approach.