Abstract
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.
Highlights
Visual object tracking is a crucial research problem in computer vision and has many applications including video surveillance, traffic monitoring, robotics and human computer interface
This paper proposed a robust and efficient scale-adaptive tracker in tracking-bydetection framework, 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
We propose a reliability weight w for sub-part trackers. w endues multi-part tracker the ability to identify whether the object is occluded or not, and multi-part tracker can select the optimal tracker for different frame itself
Summary
Visual object tracking is a crucial research problem in computer vision and has many applications including video surveillance, traffic monitoring, robotics and human computer interface. Jeong et al.[10] applies a naive multi-block scheme based on DSST[7] These methods can solve partial occlusion to a large extent. Most existing trackers fail to handle large scale variations in complex videos To address this issue, this paper proposed a robust and efficient scale-adaptive tracker in tracking-bydetection framework, 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. This paper proposed a robust and efficient scale-adaptive tracker in tracking-bydetection framework, 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. The new Gaussian kernel functions are introduced in this paper
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