点击次数：

论文类型：期刊论文

发表时间：2015-09-01

发表刊物：IEEE TRANSACTIONS ON CYBERNETICS

收录刊物：SCIE、EI、Scopus

卷号：45

期号：9

页面范围：1838-1850

ISSN号：2168-2267

关键字：Cosine similarity; discriminative weights; local similarity; object tracking

摘要：In this paper, we propose a novel weighted local cosine similarity (WLCS) and apply it to visual tracking. First, we present the local cosine similarity to measure the similarities between the target template and candidates, and provide some theoretical insights on it. Second, we develop an objective function to model the discriminative ability of local components, and use a quadratic programming method to solve the objective function and to obtain the discriminative weights. Finally, we design an effective and efficient tracker based on the WLCS method and a simple update manner within the particle filter framework. Experimental results on several challenging image sequences show that the proposed tracker achieves better performance than other competing methods.