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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Automated Recognition of Sequential Patterns in Captured Motion Streams
verfasst von : Liqun Deng, Howard Leung, Naijie Gu, Yang Yang
Erschienen in: Web-Age Information Management
Verlag: Springer Berlin Heidelberg
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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 breakpoints between them. It is challenging to learn the captured motions as it requires both segmentation and recognition. In this paper, a new method based on an extension of open-end dynamic time warping (OE-DTW) 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 applied our method on different classes of dance motions and demonstrated the effectiveness of our method by comparing with existing approach.