Particle Filter and Mean Shift Tracking Method Based on Multi-feature Fusion
基于多特征融合的均值迁移粒子滤波跟踪算法

Object tracking by using single color feature results in a poor performance in robustness. To solve this problem, an object tracking method based on multi-features fusion is presented. The proposed method uses the color and texture features extracted by Local Binary Pattern(LBP) to present the interested target, performs a feature fusion in mean-shift and particle filter algorithms, and efficiently avoids the unstable problems via using single color feature for representation. The two common used fusion rules are used,thus overcoming the degeneracy problem and resulting in low computational cost. Experimental results indicate the proposed method is more robust to present object and has good performance in complex scene.