Abstract
Multiple moving object tracking is a challenging problem is multi-image fusion in Robotic Space. To handle this, particle filter and SOM are used to track the system state based on information from multiple cameras. The random moving object is tracked based on the integrated information from distributed network cameras. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in Robotic Space, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into SOM(Self Organizing Map) based particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-motion 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 the experiments.
