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论文类型：期刊论文

发表时间：2017-12-01

发表刊物：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

收录刊物：SCIE、EI

卷号：27

期号：12

页面范围：2567-2577

ISSN号：1051-8215

关键字：Dictionary learning; gradient descend; max margin; visual tracking

摘要：In this paper, we present a discriminative tracking method based on dictionary learning and support vector machine (SVM) classification, where the dictionary and the classifier are jointly learned within a unified objective function. A discriminative differential tracking method is proposed, which estimates the motion parameters iteratively by the gradient-based method to maximize the SVM classification score, leading the bounding box to move purposively. As the target appearance may change across frames, an online update scheme is exploited, which not only reserves the discriminative information, but also adaptively accounts for the appearance changes in the dynamic scenes. We examine the proposed method on the benchmark challenging image sequences, including heavy occlusion, pose change, illumination variation, and so on. Extensive evaluations demonstrate that the proposed tracker performs favorably against other state-of-the-art algorithms.