Visual tracking with astructured local model

In this paper, we propose a novel visual tracking algorithm combining appearance feature and spatial information. These two aspects extract the constant elements of the target object. To represent information of an object, we utilize a block-local model and a structured visual histogram learned from an object´s spatial information, which can adapt to changes in the appearance of the target and environment of the background. During tracking, the proposed algorithm introduces an occlusion model to prevent the environment change from affecting the target´s posterior probability. Besides, the online updating is based on incremental algorithms for principal component analysis and the renewal of a spatial histogram queue. Experimental results show that our method outperforms relative trackers.