基于Bhattacharyya系数的由粗到精的核匹配搜索方法  

A Coarse-to-Fine Searching Method with Kernel Matching Based on Bhattacharyya Coefficients

作　　者：李良福[1,2,3] 冯祖仁[2] 陈卫东[1] 郑宝忠[1] 

机构地区：[1]中国兵器工业集团第205研究所,西安710065 [2]西安交通大学系统工程研究所,西安710049 [3]陕西师范大学计算机科学学院,西安710062

出　　处：《模式识别与人工智能》2008年第4期514-519,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家863计划项目(No.2006AA04Z222);国家自然科学基金项目(No.60475023);博士点基金项目(No.20050698032);中国博士后基金项目(No.20070411127)资助

摘　　要：均值漂移算法是一种高效的模式匹配算法.在传统的均值漂移方法基础上,本文针对运动范围较大的目标跟踪问题进行研究,提出一种基于 Bhattacharyya 系数的由粗到精的核匹配搜索方法.该算法能够有效利用相似性度量函数 Bhattacharyya 系数在实现对运动目标初始的粗定位情况下,利用均值漂移方法进行迭代求解局部最优值,从而实现目标的精定位,成功实现大范围运动目标的跟踪.实验结果验证该算法在跟踪精度和速度上均优于传统方法.Mean shift is an efficient pattern match algorithm. Aiming at object tracking in large motion area, a mean shift algorithm is proposed based on coarse-to-fine searching with kernel matching. It can efficiently use a similarity measure function to realize the rough location of motion object. Then, the mean shift method is used to obtain the accurate local optimal value by iterative computing, and thus object tracking in large motion area is successfully realized. Experimental results show it has good performance in accuracy and speed compared with traditional algorithm.

关 键 词：视觉 跟踪 均值漂移 BHATTACHARYYA系数 核匹配 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]