基于分块的核窗宽调整mean-shift跟踪算法

Mean-shift Tracking Algorithm with Adjustive Bandwidth Based on Partition

针对传统mean-shift算法核窗宽在跟踪过程中无法根据目标的大小变化进行相应调整问题。本文提出了一种基于分块的核窗宽调整mean-shift跟踪算法。该算法在跟踪窗口中心与目标形心定位的基础上,对跟踪窗进行分块,根据候选目标跟踪窗和分块目标跟踪窗中心距离的变化,对核窗宽的大小进行调整,使得跟踪窗口始终与跟踪目标的大小相匹配。实验表明算法对跟踪变大和变小的目标均取得了较好的跟踪效果。

The classical mean-shift tracking algorithm with the fixed bandwidth easily leads to some problems, such as the missing of the target and the mismatching of the tracking window and the target. A new mean-shift tracking method to adjust the tracking window based on partition is proposed. After the accurate location of the target centroid, the variation of the target＇s scale can be reflected by the variation of the distance between the target centroid and the block centroids, which can be used to adjust the tracking window. The proposed approach is illustrated by some tracking examples with valid results.