奉文主要针对经典的Mean Shift跟踪算法均匀剖分整个颜色空间造成许多空的直方图区间以及不能准确表达目标

颜色分布的缺点，提出J，一种改进算法．该改进算法首先对目标的颜色进行聚类分析，根据聚类结果通过矩阵分解和正交变换

自适席地剖分日标的颜色空间从向确定对戍于每一聚类的子空间．在此基础上定义 一种新的颜色模型，该模型统计落入每

一颜色子空间的像素的加权个数并用高斯分布建模每一个子空间的颜色分布，并推导r一种相似性度量米比较目标和候选目

标的颜色模型之间的相似程度．最后基于该颜色模型提出J，改进算法．实验表 ，基于该颜色模型的改进算法比经典的Mean

Shift算法具有更好的性能，向跟踪时间与经典算法大致相 ．-The traditional Mean Shift tracking algorithm partitions uniformly the whole color space，leading to a great

number of void histogram bins，and is unable to represent accurately the color distribution of the object．To address

the problems，we present an improved algorithm．Firstly the object color is analyzed using a clustering algorithm，and

according to the clustering result the color space of the object is partitioned into subspaces by matrix factorization and

orthonormal transformation．Then a new color model is defined by considering the weighted number of pixels as well as

within—cluster distribution with Gaussian ．and a novel measure iS derived to evaluate the similarity between the reference

color model and the candidate mode1．Finally an improved algorithm is proposed based on the color mode1．Experiments

show that the improved algorithm has better performance than and is computationally comparable to the conventional

mean shift algorithm．