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

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Date Posted: 29 February 2016