Alternative TitleTarget tracking using high-dimension data clustering
Shao Chunyan1; Ding Qinghai2; Luo Haibo1; Li Yulian3
Source Publication红外与激光工程
Other AbstractInspired 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 comers 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 carded 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.
Keyword高维数据聚类 仿射形变 目标跟踪 刚体
Indexed ByCSCD
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Document Type期刊论文
2.Space Star Technology Co., Ltd
Recommended Citation
GB/T 7714
Shao Chunyan,Ding Qinghai,Luo Haibo,等. 采用高维数据聚类的目标跟踪[J]. 红外与激光工程,2016,45.0(004):0428002-1.
APA Shao Chunyan,Ding Qinghai,Luo Haibo,&Li Yulian.(2016).采用高维数据聚类的目标跟踪.红外与激光工程,45.0(004),0428002-1.
MLA Shao Chunyan,et al."采用高维数据聚类的目标跟踪".红外与激光工程 45.0.004(2016):0428002-1.
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