针对视频序列中目标的跟踪，均值漂移算法和卡尔曼滤波器相结合的目标跟踪算法已经被提出，而在移动机器人上实现对机动目标的实时跟随时，机器人自身的运动引起目标在像平面的偏移不能被忽略，在详述了两者的关系的基础上，建立起以机器人一个周期内的运动作为输入量的状态方程，以卡尔曼滤波器的估计值作为均值漂移算法的启动点，均值漂移算法的最终收敛点作为每帧的跟踪结果，并以此收敛点替代滤波器的估计值，两种算法交替使用，互为补充。实验表明所提算法可以实现在室外环境下对动态目标的实时跟踪。

Aimed at target tracking in the video image sequences, the tracking algorithm which combines the Mean shift algorithm and Kalman filtering has been proposed, but the offset in the image plane caused by the motion of the robot can not be ignored when implementing the real-time moving object following with a mobile robot. Based on the description of the relationship between the offset and the motion, the target dynamics with the motion of robot as the external control is depicted, and the algorithm that combines the Mean shift and Kalman filter in a novel way is proposed. With the state estimation of Kalman filter as the starting position of the Mean shift and with the converge location of the Mean shift as tracking results of current frame, the state estimation is replaced with the converge location of the Mean shift. Two algorithms work alternately and interact with each other. Experiment results indicate that the proposed algorithm is real-time and robust on dynamic target tracking under complex outdoor environment.