Abstract
For the problem that the correlation filter (CF) trackers can not effectively deal with the occlusion and cause the loss of the target, a large number of tracking algorithm improvements focus on the combination of more powerful features to enrich the apparent model of the target. However, this only helps to discriminate the target from background within a small neighborhood. In this paper, an improved context-aware correlation filtering framework is introduced, which can comprehensively integrate global context information in the correlation filter tracker to effectively deal with the target's fast motion, occlusion and other issues. And the criterion APCE is used to judge the reliability of the tracking result, thus adjusting the threshold adaptively for model updating. A large number of experiments demonstrate that this framework has a significant impact on the performance of many CF trackers with only a modest impact on frame rate.
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