基于模糊C均值的Mean-Shift目标跟踪算法  

Mean-Shift tracking algorithm based on FCM

作　　者：田存伟[1] 葛广英[1] 申哲[1] 

机构地区：[1]聊城大学物理科学与信息工程学院,山东聊城252059

出　　处：《计算机应用》2009年第12期3332-3335,共4页journal of Computer Applications

摘　　要：针对Mean-Shift算法核函数带宽固定的缺陷,提出一种基于模糊C均值(FCM)的Mean-Shift目标跟踪算法。该算法采用FCM算法在YCrCb颜色空间对运动目标及附近背景进行分割,根据分割后的目标像素点统计量,遵循相邻两帧图像中目标大小不会突变的原则,修正Mean-Shift核函数窗宽。实验结果表明,该算法能够准确高效地对运动目标进行跟踪,对尺寸逐渐减小和逐渐增大的目标都能实现自动调整跟踪窗大小。Concerning the bug of the kernel function bandwidth in traditional Mean-Shift tracking algorithm, a new Mean- Shift tracking algorithm was presented using Fuzzy C-Means （FCM）. FCM clustering was used to segment the moving target from background in the YCrCh color-space. According to the rules that the areas of the target in adjoining frames are not changed abruptly, the bandwidth of kernel function was corrected with the statistic of segmented target pixels. Experimental results show that this algorithm can track object accurately and effectively. The sizes of the tracking windows could be adjusted automatically to adapt to the decreasing or increasing sizes of the moving target.

关 键 词：目标跟踪 Mean—Shift算法 模糊C均值聚类 图像分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]