基于改进的Mean Shift鲁棒跟踪算法

Mean Shift跟踪算法在目标尺度变化大和被遮挡时存在较大的缺陷。针对这一问题,提出了一种基于多级正方形匹配的自适应带宽选择和分块抗遮挡的目标跟踪算法。该算法采用目标中心点的离散程度和增量试探法计算出可能的变化尺度,然后采用多级正方形匹配法预测目标的运动趋势,将巴氏系数最大者的尺度作为Mean Shift核函数新的带宽。同时,对前景目标进行分块,根据子块的遮挡程度自适应改变子块权重并按一定准则融合有效子块的跟踪结果。实验结果表明,该算法具有很好的鲁棒性。

A robust tracking algorithm based on improved Mean Shift

The Mean Shift algorithm has a defect in handling moving targets with large scale change or being obscured. In order to solve this problem, we propose a bandwidth adaptive and anti blocking tracking algorithm based on multi-level square matching and fragment. The proposed algorithm uses the centroid deviation of the target model and the bandwidth trials method to compute the possible scales. The motion trend of the target is predicted through the multi level square matching method, and the scale of the largest Bhattacharyya distance of the candidate targets is selected as the new bandwidth of the Mean Shift kernel function. At the same time, we divide the target into several fragments, adaptively change their weights according to the degree of being obscured, and then fuse the results of effective fragments under certain rules. Experimental results show that this algorithm has good robustness performance on tracking targets.