Abstract
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.
Highlights
Visual object tracking has attracted much attention in computer vision and robotics communities, which enjoys a wide range of applications such as traffic control, medical imaging, surveillance, and auto-control systems
We propose a kernelized correlation filters (KCF)-based visual tracker via holistic and reliable local parts
The proposed KCF-HR tracker consists of local part level and holistic level
Summary
Visual object tracking has attracted much attention in computer vision and robotics communities, which enjoys a wide range of applications such as traffic control, medical imaging, surveillance, and auto-control systems. Given the initial state (e.g., position and extent) of a target object in the first image, the goal of tracking is to estimate the states of the target in the subsequent frames.[1,2] Despite having achieved considerable progress over the past decade, effective modeling of the appearance of tracked objects remains a challenging problem due to visual appearance changes,[3,4] such as illumination variation (IV), partial and heavy occlusion, background clutters (BC), motion blur (MB), deformation, and low resolution (LR) As a result, it remains a hot area of research to design a robust visual tracker. The KCF-HR tracker handles BC and other challenging appearance variations well because it employs the holistic and reliable-parts-based schema and the adaptive updating schema, which can reduce the risk of drifting and eliminate most of the effects of the background and appearance variations
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