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论文类型：期刊论文

发表时间：2016-08-01

发表刊物：IEEE TRANSACTIONS ON CYBERNETICS

收录刊物：SCIE、EI、Scopus

卷号：46

期号：8

页面范围：1914-1925

ISSN号：2168-2267

关键字：Feature selection; hash functions; object tracking

摘要：In this paper, we propose a novel tracking framework based on discriminative supervised hashing algorithm. Different from previous methods, we treat tracking as a problem of object matching in a binary space. Using the hash functions, all target templates and candidates are mapped into compact binary codes, with which the target matching is conducted effectively. To be specific, we make full use of the label information to assign a compact and discriminative binary code for each sample. And to deal with out-of-sample case, multiple hash functions are trained to describe the learned binary codes, and group sparsity is introduced to the hash projection matrix to select the representative and discriminative features dynamically, which is crucial for the tracker to adapt to target appearance variations. The whole training problem is formulated as an optimization function where the hash codes and hash function are learned jointly. Extensive experiments on various challenging image sequences demonstrate the effectiveness and robustness of the proposed tracker.