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JRM Vol.17 No.6 pp. 664-671
doi: 10.20965/jrm.2005.p0664
(2005)

Paper:

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Human Posture Probability Density Estimation Based on Actual Motion Measurement and Eigenpostures

Tatsuya Harada*, Taketoshi Mori**, and Tomomasa Sato*

*Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

**Graduate School of Interdisciplinary Information Studies, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Received:
January 27, 2005
Accepted:
May 13, 2005
Published:
December 20, 2005
Creative Commons License
Keywords:
human motion, motion observation, motion capture, motion segmentation, motion database
Abstract
We construct human posture probability density based on actual human motion measurement. Human postures in daily life were measured for two days by having subjects wear a mechanical motion capture device. Accumulated human postures were converted to unit quaternions to guarantee the uniqueness of posture representation. To represent probability density effectively, we propose eigenpostures for posture compression and use the kernel-based reduced set density estimator (RSDE) to reduce the number of posture samples and construction of posture probability density. Before compression, unit quaternions were converted to Euclidean space by logarithmic mapping. After conversion, postures were compressed in Euclidean space. Applying constructed human posture probability density for unlikely posture detection and motion segmentation, we verified its effectiveness for many different applications.
Cite this article as:
T. Harada, T. Mori, and T. Sato, “Human Posture Probability Density Estimation Based on Actual Motion Measurement and Eigenpostures,” J. Robot. Mechatron., Vol.17 No.6, pp. 664-671, 2005.
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