CamShift算法应用于复杂背景及遮挡条件下视频跟踪时，极易出现跟踪失效和目标丢失。本文提出基于颜色、纹理及目标运动信息的综合特征用于改进 CamShift算法，结合 Kalman滤波器对目标运动状态进行预测提高了复杂背景下运动目标的跟踪稳定性和跟踪精度。在目标发生遮挡时，通过目标遮挡前的先验信息进行最小二乘拟合及目标运动轨迹外推，预测目标运动位置信息，有利于遮挡结束时对运动目标的重新捕获。多组实验结果及性能分析表明，该算法在复杂背景及目标被短时遮挡情况下，可以实现目标的持续、稳定跟踪，并具有较好的实时性。

Traditional CamShift algorithm has been widely applied in the field of video tracking, but it tends to fail in the complex background and occlusion condition. In this paper, we choose color, texture, and the target motion information as features based on traditional CamShift algorithm, and constantly predict the target state of motion combined with the Kalman filter, in order to improve tracking accuracy in the complex background. In the event of occlusion, we use least squares fitting and extrapolation to predict the target location through the priori motion information of the target before occlusion, and re-capture the target after occlusion. The experimental results show that the algorithm can still track the target well in the case of complex background and short-term occlusion and have good real time ability.