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Visual tracking is a challenging task in computer vision due to various appearance changes of the target. Although correlation filter-based trackers have achieved competitive results, they may easily lead to tracking failure because of the high sensitivity of correlation filter to occlusion. Part-based correlation filter trackers can deal with partial occlusion to some extent, but they may easily drift to the background in the case of fast motion or heavy occlusion. To better solve the above-mentioned problems, a kernelized correlation filter-based tracker that processes both holistic and reliable local parts is proposed. For local parts, reliable parts are identified by peak-to-sidelobe ratio. When all parts are unreliable, we propose to apply a sliding window on each part to generate patches, among which a reliable patch is identified, and the part is replaced by the reliable patch. In holistic level, holistic tracking is performed with the rough position voted by reliable local parts, and then the holistic tracking result is used to provide feedback for parts to update its scale and filter. Moreover, we propose to reset unreliable parts when the holistic tracking result is reliable. The experimental results illustrate that the proposed tracker outperforms those of several state-of-the-art trackers.

Correlation filter-based visual tracking via holistic and reliable local parts