关键词: 压缩传感, 压缩跟踪, 多示例学习, 样本集, 分类器

Abstract: The compressive tracking algorithm in the tracking process can cause the failure when the target moves fast and has big changes in appearance. In order to solve these problems, a developed compressive tracking algorithm is proposed. Firstly, the sample sets are improved, besides the positive and negative sample sets, this paper also collects one additional candidate sample set, which can effectively reduce the error of gathering samples. Secondly, referencing the MIL algorithms, strong classifier is constituted by the assembled weak classifiers, and it will determine the target through calculating the candidate sample sets, it also proposes new methods which can update the learning rate and the strong classifier. Finally, it uses the probability values that the candidate sample set is positive sample set to adaptively adjust the size of the target window. Experiments show that the improved algorithm has an accurately tracking result for the fast moving and great changed targets, and it also has better robustness than the other algorithms.

Key words: compressive sensing, compressive tracking, Multiple Instance Learning（MIL）, samples set, classifiers