一种基于卡尔曼预测的mean shift跟踪算法

A mean shift tracking method combines with the prediction of Kalman

针对传统的mean shift算法在复杂场景下跟踪目标易丢失的问题,提出一种mean shift算法与卡尔曼预测相结合的算法.根据mean shift跟踪位置与卡尔曼预测位置的比较,确定最终的跟踪位置,并将其作为下一次跟踪的起点,跟踪结束后显示车辆运动的轨迹.实验结果表明:当目标与背景的颜色相近时,该文算法更具鲁棒性,且仍可满足实时性的要求.

In complex cases, the traditional mean shift algorithm easily loses the target. An algo- rithm which combines the mean shift and Kalman prediction algorithm is proposed. By comparing the location of mean shift tracking with Kalman prediction, the algorithm determines the final tracking position which is regarded as the starting point of the next tracking, and the trajectory of vehicle motion is shown after tracking is finished. The experimental results show that the algorithm improves the problems of easy losing tracking target under complex scenarios, and the proposed algorithm can still meet the requirement of real-time.