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Visual tracking is challenging due to appearance changes caused by motion, illumination, occlusion and pose, among others. For these local changes, appearance model based tracking algorithms, such as MILtracker [8], have adopted local features and most recently extended to compressive domain, namely Compressive Tracking [13], for the real-time performance. However, the motion information is missed...
Visual tracking is challenging due to appearance changes caused by motion, illumination, occlusion and pose, among others. For these local changes, appearance model based tracking algorithms, such as MILtracker [8], have adopted local features and most recently extended to compressive domain, namely Compressive Tracking [13], for the real-time performance. However, the motion information is missed...
In this paper, we propose online metric learning tracking method that consider visual tracking as a similarity measurement problem, and incorporates adaptive metric learning and generative histogram model based on non-sparse linear representation into the target tracking framework. We propose a generative histogram model based on non-sparse linear representation, which make full use of the non-sparse...
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