采用高维数据聚类的目标跟踪

根据刚体各部位具有变换一致性这一特性,提出一种采用高维数据聚类的目标跟踪方法。从数学理论方面证明提出的度量方法可以应用于目标跟踪, 称其为高维数据聚类跟踪器(HDDC tracker)。该算法框架如下,首先, 采用Harris检测器对模板与跟踪区域进行特征提取;然后利用这些特征的空间信息对所提取的特征进行编组;接着计算模板特征组与跟踪区域特征组间的仿射变换阵;最后,采用高维数据聚类对这些仿射变换阵进行度量, 将那些相似仿射阵对应的跟踪区域作为跟踪目标。实验表明: HDDC tracker能够有效地跟踪具有仿射形变的目标,并且性能优于先进跟踪算法。

Target tracking using high-dimension data clustering

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.