为提高目标跟踪算法在复杂条件下的鲁棒性和准确性，研究了一种基于贝叶斯分类的结构稀疏表示目标跟踪算法。首先通过首帧图像获得含有目标与背景模板的稀疏字典和正负样本；然后采用结构稀疏表示的思想对样本进行线性重构，获得其稀疏系数；进而设计一款贝叶斯分类器，分类器通过正负样本的稀疏系数进行训练，并对每个候选目标进行分类，获得其相似度信息；最后采用稀疏表示与增量学习结合的方法对稀疏字典进行更新。将该算法与其他4种先进算法在6组测试视频中进行比较，实验证明了该算法具有更好的性能。%In order to enhance the robustness and precision of tracking algorithm in complex scenarios, this paper pro-poses 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 sam-ples. 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.
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