关键词: 目标跟踪, 粒子滤波, 稀疏表示, 字典, 贝叶斯分类

Abstract: In order to enhance the robustness and precision of tracking algorithm in complex scenarios, this paper proposes a Bayes classification based structured sparse representation object tracking algorithm. Firstly, in the first frame, a sparse dictionary is obtained, which contains target and background templates, as well as positive and negative samples. Secondly, all samples are linearly combined based on the idea of structured sparse representation, hence the coding coefficients can be gotten. Thirdly, a kind of Bayes classifier is designed, which is trained by the coding coefficients of positive and negative samples. The classifier is able to detect the candidate target and obtain the likelihood information of them. Fourthly, the dictionary is updated by combining incremental subspace learning and sparse representation method together. Finally, the proposed tracker performs favorably against 4 state-of-the-art trackers on 6 challenging video sequences.

Key words: object tracking, particle filter, sparse representation, dictionary, Bayes classification