Head tracking method based on multi-feature fusion
基于多特征融合的头部跟踪方法研究*

In order to effectively solve the poor performance of head tracking,this paper proposed an new method based fusing measurements of head by using D-S evidence theory. It used Mean-Shift algorithm to produce more effective particles that approache the real posterior distribution in the framework of particle filter. The proposed method used the color and distance to maximum gradient point (DMG) features as the observation model, and efficiently avoided the unsatble problems via using single color feature in the illumination of mutation, posture change, greater distance and similar background. Experiment results indicate the proposed method is more robust to present object and has good performance in complex scene.