MEAN-shift tracking algorithm with weight fusion strategy
Lingfeng Wang, Chunhong Pan, Shiming Xiang · 2011
In this paper, we propose a new Mean-shift algorithm to tackle some tracking difficulties, such as background clutter and partial occlusion. First, we compare all Mean-shift-like tracking algorithms, and indicate that the main difference among them is weight calculation. Then, a new fusion strategy is proposed to unify all weight calculation methods into a framework. Based on this framework, we propose a novel weight calculation method, which takes the candidate model into consideration as well as incorporates the local background. Extensive experiments are conducted to evaluate the proposed approach. Comparative experimental results indicate that the tracking accuracy is improved as compared with the state-of-the-arts.