Automated Recognition of Sequential Patterns in Captured Motion Streams

摘要：

Motion capture data has been frequently used in computer animation and video games. Motions are often captured in a continuous manner such that a motion contains multiple patterns joined sequentially without obvious break points between them. It is challenging to learn the captured motions as it re quires both segmentation and recognition. In this paper, a new method based on an extension of open-end dynamic time warping (OEDTW) is proposed to automatically segment and recognize sequential patterns in motion streams. To enhance the performance, we introduce a global constraint of K-Repetition on OE-DTW and a flexible end point detection scheme. In the experiments, we ap plied our method on different classes of dance motions and demonstrated the ef fectiveness of our method by comparing with existing approach.

关键词： Dance motion sequential pattern segmentation and recognition continuous dynamic programming global constraint open-end DTW

作者: Liqun Deng Howard Leung Naijie Gu Yang Yang

作者单位: University of Science and Technology of China, Hefei, China City University of Hong Kong, Hong Kong City University of Hong Kong, Hong Kong University of Science and Technology of China, Hefei, China

会议类型: 国际会议

会议名称: 11th International Conference,WAIM 2010(第十一届网络时代管理国际会议)

会议地点: 九寨沟

会议语种:英文

页码: 250-261

在线出版日期: 2010-07-14（万方平台首次上网日期，不代表论文的发表时间）