Study on Target Model Update Method in Mean Shift Algorithm
Shen Zhi · Acta Automatica Sinica · 2009
Mean shift is a robust and real-time pattern matching algorithm.At present,the total model update strategy of the mean shift algorithm still has shortage under changed scenes,e.g.,target appearance changes,non-target occlusion.Therefore,the paper presents a selective sub-model update strategy for the mean shift algorithm.The proposed method treats each sub-model of target model as singleton,selects and updates sub-model and its weight based on match contributing degree of each sub-model in the current frame.The experiment result shows the proposed method is more robust and effective than the total model update strategy.