Human motion database with a binary tree and node transition graphs


Database of human motion has been widely used for recognizing human motion and synthesizing humanoid motions. In this paper, we propose a data structure for storing and extracting human motion data and demonstrate that the database can be applied to the recognition and motion synthesis problems in robotics. We develop an efficient method for building a human motion database from a collection of continuous, multi-dimensional motion clips. The database consists of a binary tree representing the hierarchical clustering of the states observed in the motion clips, as well as node transition graphs representing the possible transitions among the nodes in the binary tree. Using databases constructed from real human motion data, we demonstrate that the proposed data structure can be used for human motion recognition, state estimation and prediction, and robot motion planning.

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  1. Arikan, O., & Forsyth, D. A. (2002). Synthesizing constrained motions from examples. ACM Transactions on Graphics, 21(3), 483–490.

  2. Artac̆, M., Jogan, M., & Leonardis, A. (2002). Incremental PCA or on-line visual learning and recognition. In Proceedings of the 16 th international conference on pattern recognition (pp. 30 781–30 784).

  3. Bentivegna, D., Atkeson, C., & Cheng, G. (2004). Learning tasks from observation and practice. Robotics and Autonomous Systems, 47(2–3), 163–169.

  4. Billard, A., Calinon, S., & Guenter, F. (2006). Discriminative and adaptive imitation in uni-manual and bi-manual tasks. Robotics and Autonomous Systems, 54, 370–384.

  5. Brand, M., & Hertzmann, A. (2000). Style machines. In Proceedings of SIGGRAPH 2000 (pp. 183–192).

  6. Breazeal, C., & Scassellati, B. (2002). Robots that imitate humans. Trends in Cognitive Science, 6(11), 481–487.

  7. Ijspeert, A., Nakanishi, J., & Schaal, S. (2002). Movement imitation with nonlinear dynamical systems in humanoid robots. In Proceedings of international conference on robotics and automation (pp. 1398–1403).

  8. Inamura, T., Toshima, I., Tanie, H., & Nakamura, Y. (2004). Embodied symbol emergence based on mimesis theory. International Journal of Robotics Research, 24(4/5), 363–378.

  9. Kadone, H., & Nakamura, Y. (2005). Symbolic memory for humanoid robots using hierarchical bifurcations of attractors in nonmonotonic neural networks. In Proceedings of international conference on intelligent robots and systems (pp. 2900–2905).

  10. Kittler, J., & Illingworth, J. (1986). Minimum error thresholding. Pattern Recognition, 19(1), 41–47.

  11. Kovar, L., Gleicher, M., & Pighin, F. (2002). Motion graphs. ACM Transactions on Graphics, 21(3), 473–482.

  12. Kulić, D., Takano, W., & Nakamura, Y. (2008). Incremental learning, clustering and hierarchy formation of whole body motion patterns using adaptive hidden Markov chains. International Journal of Robotics Research, 27(7), 761–784.

  13. Lee, J., Chai, J., Reitsma, P. S. A., Hodgins, J. K., & Pollard, N. S. (2002). Interactive control of avatars animated with human motion data. ACM Transactions on Graphics, 21(3), 491–500.

  14. Nakamura, Y., & Hanafusa, H. (1986). Inverse kinematics solutions with singularity robustness for robot manipulator control. Journal of Dynamic Systems, Measurement, and Control, 108, 163–171.

  15. Safonova, A., & Hodgins, J. (2007). Interpolated motion graphs with optimal search. ACM Transactions on Graphics, 26(3), 106.

  16. Schaal, S., Ijspeert, A., & Billard, A. (2003). Computational approaches to motor learning by imitation. Phylosophical Transactions of the Royal Society of London B: Biological Sciences, 358, 537–547.

  17. Sidenbladh, H., Black, M., & Sigal, L. (2002). Implicit probabilistic models of human motion for synthesis and tracking. In European conference on computer vision (pp. 784–800).

  18. Vaishnavi, V. K. (1989). Multidimensional balanced binary trees. IEEE Transactions on Computers, 38(7), 968–985.

  19. Ward, J. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.

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Correspondence to Katsu Yamane.

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Yamane, K., Yamaguchi, Y. & Nakamura, Y. Human motion database with a binary tree and node transition graphs. Auton Robot 30, 87–98 (2011).

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  • Motion database
  • Binary tree
  • Human motion recognition
  • Humanoid motion planning