Abstract
We present a method for representing tracking and human-following by fusing distributed multiple vision systems in intelligent space, with applications to pedestrian tracking in a crowd. In this context, particle filters provide a robust tracking framework under ambiguous conditions. The particle filter technique is used in this work, but in order to reduce its computational complexity and increase its robustness, we propose to track the moving objects by generating hypotheses not in the image plan but on a top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multiobject tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment, and its performance is verified by experiments.
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