The Mean-Shift algorithm often fails when tracking a target with a high speed,a large change of scale or an occlusion.To tackle the problem,taking advantage of optical flow method,we propose an adaptive Mean-Shift object tracking algorithm in this paper.This method is based on the mean drift vector of the tracking window center,and Bhattacharyya coefficient based dichotomy is used to get both width and height of the window.Especially,the optical Flow method is employed to fine-tune the window position and window size according to the information of feature points.To track targets which are occluded by immobile objects,we use the color difference to observe the occlusion zone,and catch the target with Bhattacharyya coefficient when it is unsheltered.Experimental results show that the tracking algorithm has a very good effect in some cases.Besides,the proposed tracking algorithm has been successfully applied to rail tracking,and the application demonstrates that the algorithm can significantly improve the rail tracking reliability.