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Incorporating metric learning in visual tracking applications has been demonstrated to be able to improve tracking performance. However, the optimal metric is mainly derived based on annotated feature vectors by studying their magnitude and intersection angle. In complex scenarios, the magnitude of feature samples may change drastically, confining the matching performance of the distance metric. Moreover,...
Recent attempts demonstrate that learning an appropriate distance metric in visual tracking applications can improve the tracking performance. However, the existing metric learning methods learn and adjust the distance between all pairwise sample points in an iterative way, which raises the time consumption issue in real-time tracking applications. To address this problem, this paper proposes a novel...
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