关键词: 压缩感知, 目标跟踪, 互补特征, 特征加权, 大权值特征

Abstract: As target tracking algorithm based on compressive sensing can extract few features and fails to track targets stably when textures or lightings change much, a target tracking algorithm based on the complementary feature weighting of compressive sensing is proposed. The algorithm extracts two types of complementary texture features and gray average features using two random measurement matrices, and calculates these two types of features’ weight according to the classification results, using the selected large weights feature to find the target in next frame. Because the feature stability is different in track processing, different update levels are taken. Results of tests on variant video sequences show that the proposed algorithm is capable of accurately capturing the tracking target, and obtained results satisfy the requirements of real-time tracking. Compared with the related algorithms, the proposed algorithm can hold a stronger robustness when target textures or lightings change much.

Key words: compressive sensing, target tracking, complementary feature, multi-feature weighting, large weights feature