An improved TLD method based on color feature
Jianghai Hu, Meng Cai, Jianxun Li · 2017
Visual object tracking is a significant issue in the task of following a target in a stream of images. In this paper, we address this problem by proposing a novel parallel frame based on the original tracking-learning-detection method. The detector and the tracker in our algorithm are working simultaneously and output the candidate regions, then we perform analysis on these regions based on its color feature in HSV color space and the final position of target can be obtained naturally. Besides, this paper introduces BRISK into our tracker to alleviate the instability caused by target rotation and illumination variation. Extensive experimental results on massive benchmark datasets demonstrate that our algorithm has a crucial improvement over the original TLD and other state-of-the-art algorithms.