Multi-part and scale adaptive visual tracker based on kernel correlation filter

Accurate visual tracking is a challenging issue in computer vision. Correlation filter (CF) based methods are sought in visual tracking based on their efficiency and high performance. Nonetheless, CF-based trackers are sensitive to partial occlusion, which may reduce their overall performance and even lead to failure in tracking challenge. In this paper, we presented a very powerful tracker based on the kernelized correlation filter tracker (KCF). Firstly, we employ an intelligent multi-part tracking algorithm to improve the overall capability of correlation filter based tracker, especially in partial-occlusion challenges. Secondly, to cope with the problem of scale variation, we employ an effective scale adaptive scheme, which divided the target into four patches and computed the scale factor by finding the maximum response position of each patch via kernelized correlation filter. With this method, the scale computation was transformed into locating the centers of the patches. Thirdly, because the small deviation of the central function value will bring the problem of location ambiguity. To solve this problem, the new Gaussian kernel functions are introduced in this paper. Experiments on the default 51 video sequences in Visual Tracker Benchmark demonstrate that our proposed tracker provides significant improvement compared with the state-of-art trackers.