以核颜色直方图为跟踪特征的均值移动算法易受环境光照、视角和摄像机参数等因素的影响。根据灰度共生矩阵的思想构造了核共生矩阵来描述目标模型和候选目标，并在此基础上提出了一种基于核共生矩阵的均值移动跟踪算法。在算法的实现过程中做了一些改进工作：构造核共生矩阵时对相反方向上的像素加以区分，从而更好地刻画目标的不对称特性；将目标模型和候选目标的核共生矩阵规整到同一常数以提高计算精度；对各像素权值的计算公式进行修正以提高算法速度。光照较暗，照度变化和存在部分遮挡等条件下的真实场景实验结果表明，该算法在这些情况下仍能有效地跟踪目标。

The performance of mean shift algorithm using kernel histograms as tracking cues is always affected by illumination, visual angle and camera parameters. Kernel co-occurrence matrices (KCM) constructed on the concept of gray level co-occurrence matrix (GLCM) were used to represent the target model and the target candidate. Then those matrices were employed as the tracking features in mean shift tracking framework. Some improvements were made in the implementation of the algorithm. First, pixels on the opposite position of the current pixel were treated discriminately to depict the asymmetric characteristics of the object. Second, the KCMs of the target model and the target candidate were normalized to a same integer to improve calculation accuracy. Third, the computation of each pixel weight was modified to improve operation speed. The tracking results of several real world sequences with dark or changing illumination and partial occlusion show that the proposed algorithm can track the target effectively.