Memory-based state estimation for handling occlusion during object tracking by Particle Filter

Abstract

Particle Filter is one of the most widely used algorithm in object tracking, because it can handle the nonlinear and/or non-Gaussian problems. However it fails when the tracked object is occluded by other objects. In order to solve such problems, in this paper, a memory-based state estimation scheme is introduced into the particle filter to estimate the state of the occluded object. When occlusion occurs, the tracked object is located by the states stored in the memory space. And the tracked object can be correctly relocated when recovered from the occlusion. The experimental results show the effects of our proposed method.