A Multiple Features Image Tracking Algorithm

In Mean Shift algorithm, the features of the tracked target and the image matching similarity criterion have great influence on the result of tracking. a new algorithm of target tracking is proposed. the algorithm combine local binary pattern and color information to form a new feature CL, which tracks target by using a method of centroid iteration based on maximum posterior probability. Thanks to the simplification of the LBP, the CL has higher differentiation ability and lower computational complexity. Experimental results show that the new algorithm have significantly improved the tracking performance, in comparison with original Mean Shift algorithm. in complex background, the algorithm can track the target robustly.