Abstract: Inspired by the fact that a rigid body has consistent transformation for its individual part, a novel target tracking algorithm based on high-dimension data clustering is proposed. The proposed measure is proved to be available in object tracking mathematically. Thus, it is called the High-Dimension Data Clustering(HDDC) tracker. The frameworks of proposed method are as follows. First, Harris detector is utilized to extract the corners both in the template and the tracking region. Second, these feature points are grouped via their position information separately. Third, affine matrixes between the template and the tracking region are calculated among their respective feature groups. At last, high-dimension data clustering is carried out to measure these matrixes, and the feature points corresponding with the similar matrixes that are tracked targets. Extensive experimental results demonstrate that HDDC is efficient on measuring affine deformed objects and outperforms some state-of-the-art discriminative tracking methods.