Abstract
Object tracking stands as a cornerstone challenge within computer vision, with blurriness analysis representing a burgeoning field of interest. Among the various forms of blur encountered in natural scenes, defocus blur remains significantly underexplored. To bridge this gap, this article introduces the Defocus Blur Video Object Tracking (DBVOT) dataset, specifically crafted to facilitate research in visual object tracking under defocus blur conditions. We conduct a comprehensive performance analysis of 18 state-of-the-art object tracking methods on this unique dataset. Additionally, we propose a selective deblurring framework based on Deblurring Auxiliary Learning Net (DID-Anet), innovatively designed to tackle the complexities of defocus blur. This framework integrates a novel defocus blurriness metric for the smart deblurring of video frames, thereby enhancing the efficacy of tracking methods in defocus blur scenarios. Our extensive experimental evaluations underscore the significant advancements in tracking accuracy achieved by incorporating our proposed framework with leading tracking technologies.
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