关键词: 目标跟踪, 模板更新, 稀疏表示, 判别分析, 分类器

Abstract: The Sparse Representation Classification（SRC） has been applied in many machine vision applications including object tracking. Despite its popularity, most existing SRC methods have low tracking accuracy. To solve these problems, this paper proposes an improved version of SRC by combining Discriminative Analysis（DA） principles and sparse decomposition. The reason why DA can improve SRC has been given. It also proposes a NormalHedge based method to estimate the location and size of the target. Furthermore, it proposes a time-loop based method to update target templates. To efficiently cope with sudden disappearances and sudden tracking failures, it proposes a cooperative tracking framework which can resume tracking process after sudden tracking failure occurs. Two types of?experiments have been performed. The first type is to evaluate the proposed methods. The second type is to compare the proposed method with other classical tracking methods on six carefully selected videos. Experiment results show the proposed algorithm and framework yield robust performances under multiple challenging conditions. Limitations of the proposed method are also mentioned.

Key words: object tracking, template updating, sparse representation, discriminative analysis, classifier