Robust object tracking via multi-cue fusion

Abstract

Object tracking is one of the fundamental problems and an active research area in computer vision during the last decade. Although a wide variety of methods have been proposed, the long-term object tracking is still a challenging problem when dealing with occlusion, out-of-view, scale and illumination variation. To address these challenges, we propose a robust visual object tracking method based on binocular vision in this paper. Our method formulates the object tracking problem in a multi-cue fusion framework which allows our system recover from tracking drift and occlusion. For each channel of the binocular cameras, the coarse object state is estimated by combining the information of motion model, detection and online tracker. Stereo filter is designed to check the object candidate consistency of the two channels. The final object state estimation is determined by fusing the two-channel information and binocular geometry constraints. Experimental results demonstrate the effectiveness of proposed method.