Online object modeling method for occlusion-robust tracking

Abstract

Object tracking is often disturbed by visual occlusion. To handle this problem, we have previously proposed the tracking method by the particle filter, which switches tracking targets autonomously. This method enables the tracker to track the occluded target indirectly by switching its target to the occluder effectively. However, the color-based target model used in this method often causes inaccurate tracking because the model with only one color distribution is not necessarily sufficient. In this paper, we propose a method for online object modeling using a set of color distributions and a set of SIFT features. Since the proposed model has more color information and local texture information, it enables the tracker to recognize the target more robustly. Furthermore, this model can be created dynamically and updated in an online fashion using the graph cuts technique during tracking. Consequently it can be applied to the previously proposed tracking method with autonomous switching of targets. Experimental results show the effectiveness of the proposed method.