一种基于Kalman-mean shift的自适应跟踪算法

Bandwidth-adaptive tracking algorithm based on Kalman-mean shift method

提出了一种基于Kalman-mean shift的自适应跟踪算法。利用卡尔曼滤波器预测目标在当前时刻的起始位置,并利用互信息量与目标尺寸之间的关系,在mean shift算法中加入了一个尺度更新项,通过尺度更新对运动目标,特别是目标尺寸变化的目标进行自适应跟踪。实验表明该算法提高了mean shift跟踪算法的适应性,有效地解决了长时间跟踪过程中尺度变化目标定位困难的问题。

A bandwidth-adaptive tracking algorithm based on Kalman-mean shift method is proposed. Firstly, the starting position of mean shift is predicted by Kalman filter at present, and then a scale updating item of tracking window is appended in the mean shift algorithm based on the relation between mutual information and the object scale. Through the scale updating, the moving object, especially the object of scale variance, is adaptively tracked. Experimental resuits demonstrate that the adaptability of mean shift method is enhanced by the improved algorithm, which is effectively applied in the tracking problem for the object of scale variance in the process of long time tracking.