基于Meanshift与Kalman的视频目标跟踪算法

Algorithm for Video Object Tracking Based on Meanshift and Kalman Filter

针对传统的Meanshift方法在复杂条件下目标跟踪丢失问题,提出了一种将Meanshift与Kalman滤波器融合的视频运动目标跟踪算法。该算法可对跟踪加入运动目标预测,根据Meanshift跟踪结果判断是否开启Kalman滤波器的预测及滤波,能提高跟踪的鲁棒性。实验结果表明,该算法可以有效改善在复杂条件下的跟踪效果,具有较好的鲁棒性。

The single Meanshift tracking method always loses its target under complex condition.The Meanshift and Kalman filter were combined in tracking moving targets.The prediction on motion of targets was taken into consideration in the algorithm.To increase the tracking robustness of the algorithm,the result of Meanshift tracking was used to determine whether or not the Kalman prediction and filter should be used.Experimental results show that the proposed algorithm can effectively enhance the tracking effect under complex condition and is more robust.