传统的Mean shift 方法采用颜色直方图作为特征，以Bhattacharyya系数作为目标参考模板与当前帧中候选目标间的相似度量，通过迭代寻找距离函数的局部最小值，从而得到当前帧中的目标实际位置。由于颜色直方图仅仅描述了图像中目标的全局颜色分布而忽略了空间位置分布，使得当目标邻域中存在与目标相近似的颜色模式时，算法无法取得理想的跟踪效果。本文提出了基于核密度估计相关的距离度量，在描述参考目标和候选目标时，考虑到诸如颜色、梯度等目标像点的特征区间的同时，融入了目标像点的空间位置信息，使得跟踪算法更加稳健和精确，能够更好适应目标和背景变化复杂的应用场合。

In traditional Mean shift algorithm ,color histogram is usually used as the features vectors. And the dissimilarity between the referenced targets and the target candidates is expressed by the metric derived from the Bhattacharyya coefficients .The traditional mean shift procedure is used to find the real position of the object through looking for the regional minimum of the distance function iteratively .But there exists some limits because loss of space distribution .To overcome this problem ,the method based on correlation between kernel density estimation of tracking region is proposed. Furthermore, some experiments manifest it can improve accuracy and robustness of this tracking algorithm.