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Object tracking has been a challenge in computer vision. In this paper, we present a novel method to model target appearance and combine it with structured output learning for robust online tracking within a tracking-by-detection framework. We take both convolutional features and handcrafted features into account to robustly encode the target appearance. First, we extract convolutional features of the target by kernels generated from the initial annotated frame. To capture appearance variationdoi:10.1007/s41095-017-0087-3 fatcat:4miqlot67vhpfc2idgyag4ka5a