Center for Human Modeling and Simulation
Document Type
Book Chapter
Date of this Version
2007
Publication Source
Proceedings of the 7th International Conference on Intelligent Virtual Agents
Volume
4722
Start Page
37
Last Page
44
DOI
10.1007/978-3-540-74997-4_4
Abstract
Many applications that utilize motion capture data require small, discrete, semantic segments of data, but most motion capture collection processes produce long sequences of data. The smaller segments are often created from the longer sequences manually. This segmentation process is very laborious and time consuming. This paper presents an automatic motion capture segmentation method based on movement qualities derived from Laban Movement Analysis (LMA). LMA provides a good compromise between high-level semantic features, which are difficult to extract for general motions, and low-level kinematic features which, often yield unsophisticated segmentations. The LMA features are computed using a collection of neural networks trained with temporal variance in order to create a classifier that is more robust with regard to input boundaries. The actual segmentation points are derived through simple time series analysis of the LMA features.
Copyright/Permission Statement
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-74997-4_4
Keywords
human motion, motion capture, motion segmentation, Laban movement analysis
Recommended Citation
Bouchard, D., & Badler, N. I. (2007). Semantic Segmentation of Motion Capture Using Laban Movement Analysis. Proceedings of the 7th International Conference on Intelligent Virtual Agents, 4722 37-44. http://dx.doi.org/10.1007/978-3-540-74997-4_4
Date Posted: 29 February 2016