增强目标模型鲁棒性的Mean-shift算法  

Mean-shift Tracking with Enhanced Robustness of Target Model

作　　者：郭运艳[1] 陈恳[1] 宋康康[1] 刘哲[1] 黄小霞[1] 

机构地区：[1]宁波大学信息科学与工程学院,浙江宁波315211

出　　处：《宁波大学学报（理工版）》2012年第4期25-28,共4页Journal of Ningbo University：Natural Science and Engineering Edition

基　　金：国家自然科学基金(61071120);浙江省教育厅科研项目(Y200907622);宁波市自然科学基金(210A610109)

摘　　要：针对传统Mean-shift算法中颜色核函数直方图对目标特征描述较弱的缺点,提出了一种联合目标特征点的二维结构信息和颜色信息的Mean-shift改进算法.改进算法细化了Harris检测算子的角点响应阈值,提取出更多的目标特征点计算其方向分布,并以方向与部分颜色特征的直方图构建目标模型,该模型能显著区分目标与背景.实验对不同算法进行了仿真及性能比较,结果表明:提出的改进算法在一定的复杂场景中提高了跟踪精度,且具有较好的鲁棒性.The traditional Mean-shift cannot represent accurately the color distribution of the object. To tackle this problem, a target motion-tracking algorithm is presented based on two-dimensional structural information of the target feature points and color histogram. The improved algorithm refines the Harris operator threshold to seek more feature points, with which their orientation distribution can also be sought. The target model built by combining the orientation histogram and the partial color histogram is found to be able to separate target from background to a satisfying extent. The application results from various video sequences suggest good localization precision in object tracking, and algorithmic robustness in complex scenes.

关 键 词：Mean—shift Harris检测算子 旋转不变性 联合直方图 

分 类 号：TN919.8[电子电信—通信与信息系统]