Partial occlusion tracking with blocks and discriminative model
While tracking has been developed rapidly with the presentation of efficient algorithms recent years, some problems remain unsolved. Occlusion is a challenge for tracking especially in crowded scenes. In this paper, we address the problem by proposing a robust algorithm based on particle filter with blocks and discriminative model. Discriminative model offers accurate templates for occlusion detection by alleviating influence from background pixels. Since particle filter cannot carry out effective tracking under heavy occlusion, blocking is introduced to solve the problem by abandoning unobservable parts of the target. Experimental results show that our algorithm can work persistently and effectively under severe occlusion compared with state-of-the-arts.