An Algorithm of Mean Shift Template Update Based a Group of Kalman Filters
基于卡尔曼滤波器组的Mean Shift模板更新算法

To improve the limitation of Mean Shift lacks method of template update,an algorithm of template update based a group of Kalman filters is proposed.Probability of eigenvalue in feature space is taken as the template information.A group of filters are devised,where each filter is used to estimate the change of probability of sub-eigenvalue.All update value of template can be received by multiplying these corresponding probabilities in sub-feature space.The noise parameter of each filter would change with input data,so a novel strategy of template update according to change of residual of filters could be proposed.Experimental results show that the proposed algorithm can successfully track target under condition of changeable gesture of target and changeable illumination,and is robust to occlusion.