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Visual tracking is a significant but challenging field in computer vision. Long-term robust tracking in unconstrained environments is insurmountable due to arbitrary deformation, occlusion and background clutters. In this paper, we propose a framework to integrate couplers and historical models into a discriminative classifier for robust long-term tracking. Model updating inevitably introduces errors...
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