基于循环矩阵结构(CSK)的跟踪算法只适用于跟踪尺度固定不变的目标，为此，提出了一种基于分块的尺度自适应CSK刚体目标跟踪算法。利用各分块的空间结构推导出分块位置与目标尺度之间的内在关系，然后对各分块的跟踪结果进行置信度评价，选取高置信度的结果进行综合，有效估计目标的尺度和位置，提高了算法在遮挡、背景干扰等情况下的鲁棒性。对典型视频序列的对比试验表明，所提算法不仅能够实时跟踪目标的尺度变化，跟踪的精度和鲁棒性也明显高于原始CSK算法。

The object tracking method based on Circular Structure with Kernels (CSK) is only suitable for tracking objects with fixed scale.To solve the problem, a new patch-based scale adaptive CSK tracking method was proposed.First, the relationship between the position of patches and the scale of object was derived from spatial structure of each patch.Then, the tracking confidence level of each patch was evaluated, and those with high confidence levels were selected for integration.Therefore, effective estimation was made to the scale of the object through independent tracking of each patch and the integration.The robustness of the method under interference was improved.Experiment was made by using the typical vision sequences.The result showed that the algorithm can not only realize real-time tracking of the change of object scale,but also has higher accuracy and robustness compared with the original CSK method.