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Human Activity Recognition Based on R Transform
Wang Ying; Kaiqi Huang; Tieniu Tan
2007
会议名称CVPR workshop on the Seventh International Workshop on Visual Surveillance
会议录名称CVPR workshop on the Seventh International Workshop on Visual Surveillance
页码1-8
会议日期2007-06-01
会议地点Minneapolis, Minnesota, USA
摘要This paper addresses human activity recognition based on a new feature descriptor. For a binary human silhouette, an extended radon transform, R transform, is employed to represent low-level features. The advantage of the R transform lies in its low computational complexity and geometric invariance. Then a set of HMMs based on the extracted features are trained to recognize activities. Compared with other commonly-used feature descriptors, R transform is robust to frame loss in video, disjoint silhouettes and holes in the shape, and thus achieves better performance in recognizing similar activities. Rich experiments have proved the efficiency of the proposed method.
关键词Radon Transforms   feature Extraction   hidden Markov Models 
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12723
专题智能感知与计算研究中心
通讯作者Kaiqi Huang
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Wang Ying,Kaiqi Huang,Tieniu Tan. Human Activity Recognition Based on R Transform[C],2007:1-8.
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