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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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References
  1. [1] L. M. Tanco, and A. Hilton, “Realistic synthesis of novel human movements from a database of motion capture examples,” In Proceedings of the IEEE Workshop on Human Motion HUMO 2000, pp. 137-142, 2000.
  2. [2] J. Lee, J. Chai, P. Reitsma, J. K. Hodgins, and N. Pollard, “Interactive control of avatars animated with human motion data,” In Proceedings of SIGGRAPH 2002, pp. 491-500, 2002.
  3. [3] S. W. Lee, and K. Mase, “Activity and location recognition using wearable sensors,” IEEE Pervasive Computing, 1(3): pp. 24-32, 2002.
  4. [4] P. Gibbs, and H. Asada, “Wearable conductive fiber sensors for measuring joint movement,” In Proceedings of the IEEE International Conference on Robotics and Automation, Vol.5, pp. 4753-4758, 2004.
  5. [5] M. Isard, and A. Blake, “Condensation – conditional density propagation for visual tracking,” International Journal of Computer Vision, 29(1): pp. 5-28, 1998.
  6. [6] J. Deutscher, A. Blake, and I. Reid, “Articulated body motion capture by annealed particle filtering,” In 2000 Conference on Computer Vision and Pattern Recognition (CVPR’00), pp. 2126-2133, 2000.
  7. [7] C. Sminchisescu, and B. Triggs, “Kinematic jump processes for monocular 3d human tracking,” In 2003 Conference on Computer Vision and Pattern Recognition (CVPR’03), pp. 69-76, 2003.
  8. [8] E. Parzen, “On estimation of a probability density function and mode,” The Annals of Mathematical Statistics, 33(3): pp. 1065-1076, 1962.
  9. [9] V. Vapnik, and S. Mukherjee, “Support vector method for multivariate density estimation,” In S. A. Solla, T. K. Leen, and K.-R. Muller (editors), Advances in Neural Information Processing Systems, The MIT Press, pp. 659-665, 2000.
  10. [10] P. Mitra, C. A. Murthy, and S. K. Pal, “Density-based multiscale data condensation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(6): pp. 734-747, 2002.
  11. [11] M. Girolami, and C. He, “Probability density estimation from optimally condensed data samples,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10): pp. 1253-1264, 2003.
  12. [12] B. Schölkopf, J. C. Platt, J. S. Taylor, and A. J. Smola, “Estimating the support of a high-dimensional distribution,” Neural Computation, 13: pp. 1443-1471, 2001.
  13. [13] M. Turk, and A. Pentland, “Eigenfaces for recognition,” J. Cognitive Neuroscience, 3(1): pp. 71-86, 1991.
  14. [14] C. Bregler, “Learning and recognizing human dynamics in video sequences,” In Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, pp. 568-574, 1997.
  15. [15] N. R. Howe, M. E. Leventon, and W. T. Freeman, “Bayesian reconstruction of 3d human motion from single-camera video,” Advances in Neural Information Processing Systems, 12: pp. 820-826, 2000.
  16. [16] C. Lu, and N. J. Ferrier, “Repetitive motion analysis: Segmentation, identification, and event detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2): pp. 258-263, 2004.

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