An adaptive method of model updating in MS tracking
Ze Xian Ke, Hanhong Jiang, Chaoliang Zhang, Chunliang Jiang · 2012
Mean-Shift is an efficient algorithm in the visual tracking. The target model commonly is RGB of the object, which is composed by different components of target's color. In the tracking process, the target model should change with the change of the backgrounds. So the updating of target model is demand in mean-shift tracking. Generally the popular updating algorithm was based on the integrity of the RGB model, which can't satisfy the demands in real time. An adaptive model updating algorithm is proposed, in which the change of each component of target's RGB model is computed respectively. The components with heavily weight are select and updated. The experimental result shows that algorithm proposed in this paper is more robust in tracking for a long time.