Person tracking with partial occlusion handling

Abstract

Occlusion is a challenge for tracking especially in dynamic scene. It adds the consideration for background modeling. In the condition, the tracker will be influenced by both occlusions and background. In this paper, we address the problem by proposing a robust algorithm based on improved particle filter using discriminative model without background modeling. 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 even in dynamic scene compared with state-of-the-arts.