团块与Mean-Shift结合的局部遮挡目标跟踪

传统的基于Mean-Shift的目标跟踪方法利用目标的全局特征进行跟踪，在局部遮挡情况下跟踪效果不佳。提出一种基于团块建模和Mean-Shift相结合的利用目标局部特征的运动目标跟踪方法，对目标进行团块建模，利用Mean-shift算法对各团块进行跟踪，在此基础上确定目标新位置。该方法能够在目标发生局部遮挡时，自动选取未被遮挡的团块的跟踪结果来确定目标的位置。为了提高方法对背景干扰的鲁棒性，采用背景加权的Mean-Shift算法。实验结果表明：该方法在局部遮挡的情况下可较好地进行目标跟踪，跟踪效果优于报导的基于Mean-Shift的方法。

Tracking method for object of partial occlusion based on combination of blob modeling and Mean-Shift

Traditional Mean-Shift based object tracking adopts whole features for tracking,and is hard to track well under object occlusion.A new local feture based method is proposed,which combines the blob modeling and mean-shift together.Firstly, the blob modeling for the tracked object is built,and then each blob is tracked by the Mean-Shift method.Finally the new position of object is determined.The proposed method can select unoccluded blob for object tracking when occlusion occurs. The background-weighted Mean-Shift method is adopted to improve the robustness to the background disturbance.Experimen- tal results show that the method can track the object exactly under the circumstance of partial occlusion,and the performance is better than that of traditional Mean-Shift based method.