Object tracking algorithm based on self-adaptive selection of double histograms
基于双直方图自适应选择的目标跟踪算法*

This paper improved the traditional mean shift tracking algorithm with self-adaptive selection of both color histograms and gradient-oriented histograms, thus strengthened the robustness of novel tracking algorithm in complicated circumstance. Since the traditional mean shift tracking algorithms were usually based on one fixed color histogram, it was prone to fail when used to track changeable objects or used in changeable circumstance. In the proposed algorithm, analyzed the color and gradient orient similarities between object in present circumstance and object templates and then set a threshold so that the most effective features to present object tracking were selected, thus realizing object tracking in complicated dynamic circumstance. The reliability of the improved algorithm has been verified in serial experimental results of moving object tracking in different circumstance.