Spatial Measures between Human Poses for Classification and Understanding

  • Søren Hauberg
  • Kim Steenstrup Pedersen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7378)

Abstract

Statistical analysis of humans, their motion and their behaviour is a very well-studied problem. With the availability of accurate motion capture systems, it has become possible to use such analysis for animation, understanding, compression and tracking of human motion. At the core of the analysis lies a measure for determining the distance between two human poses; practically always, this measure is the Euclidean distance between joint angle vectors. Recent work [7] has shown that articulated tracking systems can be vastly improved by replacing the Euclidean distance in joint angle space with the geodesic distance in the space of joint positions. However, due to the focus on tracking, no algorithms have, so far, been presented for measuring these distances between human poses.

In this paper, we present an algorithm for computing geodesics in the Riemannian space of joint positions, as well as a fast approximation that allows for large-scale analysis. In the experiments we show that this measure significantly outperforms the traditional measure in classification, clustering and dimensionality reduction tasks.

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References

  1. 1.
    Bregler, C., Malik, J., Pullen, K.: Twist based acquisition and tracking of animal and human kinematics. International Journal of Computer Vision 56, 179–194 (2004)CrossRefGoogle Scholar
  2. 2.
    Carmo, M.P.D.: Differential Geometry of Curves and Surfaces. Prentice Hall (1976)Google Scholar
  3. 3.
    Erleben, K., Sporring, J., Henriksen, K., Dohlmann, H.: Physics Based Animation. Charles River Media (August 2005)Google Scholar
  4. 4.
    Grochow, K., Martin, S.L., Hertzmann, A., Popović, Z.: Style-based inverse kinematics. ACM Transaction on Graphics 23(3), 522–531 (2004)CrossRefGoogle Scholar
  5. 5.
    Guerra-Filho, G., Aloimonos, Y.: A language for human action. Computer 40, 42–51 (2007)CrossRefGoogle Scholar
  6. 6.
    Hauberg, S., Sommer, S., Pedersen, K.S.: Gaussian-Like Spatial Priors for Articulated Tracking. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 425–437. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Hauberg, S., Sommer, S., Pedersen, K.S.: Natural metrics and least-committed priors for articulated tracking. In: Image and Vision Computing (2011)Google Scholar
  8. 8.
    Kjellström, H., Kragić, D., Black, M.J.: Tracking people interacting with objects. In: IEEE CVPR (2010)Google Scholar
  9. 9.
    Lu, Z., Carreira-Perpinan, M., Sminchisescu, C.: People Tracking with the Laplacian Eigenmaps Latent Variable Model. In: NIPS, vol. 20, pp. 1705–1712. MIT Press (2008)Google Scholar
  10. 10.
    Murray, R.M., Li, Z., Sastry, S.S.: A Mathematical Introduction to Robotic Manipulation. CRC Press (March 1994)Google Scholar
  11. 11.
    Poon, E., Fleet, D.J.: Hybrid monte carlo filtering: Edge-based people tracking. In: IEEE Workshop on Motion and Video Computing, p. 151 (2002)Google Scholar
  12. 12.
    Poppe, R.: Vision-based human motion analysis: An overview. Computer Vision and Image Understanding 108(1-2), 4–18 (2007)CrossRefGoogle Scholar
  13. 13.
    Priyamvada, K.K., Kahol, K., Tripathi, P., Panchanathan, S.: Automated gesture segmentation from dance sequences. In: Int. Conf. on Automatic Face and Gesture Recognition (2004)Google Scholar
  14. 14.
    Ramanan, D., Baker, S.: Local distance functions: a taxonomy, new algorithms, and an evaluation. TPAMI (4) (2011)Google Scholar
  15. 15.
    Ripley, B.D.: Pattern recognition and neural networks. Cambridge University Press (1996)Google Scholar
  16. 16.
    Sheikh, Y.A., Khan, E.A., Kanade, T.: Mode-seeking by medoidshifts. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  17. 17.
    Shon, A.P., Grochow, K., Rao, R.P.: Robotic imitation from human motion capture using Gaussian processes (2005)Google Scholar
  18. 18.
    Sidenbladh, H., Black, M.J., Fleet, D.J.: Stochastic Tracking of 3D Human Figures Using 2D Image Motion. In: Vernon, D. (ed.) ECCV 2000, Part II. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  19. 19.
    Sminchisescu, C., Jepson, A.: Generative modeling for continuous non-linearly embedded visual inference. In: ICML 2004, pp. 759–766. ACM (2004)Google Scholar
  20. 20.
    Tenenbaum, J.B., Silva, V., Langfor, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  21. 21.
    Tournier, M., Wu, X., Courty, N., Arnaud, E., Reveret, L.: Motion compression using principal geodesics analysis. Computer Graphics Forum 28(2), 355–364 (2009)CrossRefGoogle Scholar
  22. 22.
    Urtasun, R., Fleet, D.J., Fua, P.: 3D People Tracking with Gaussian Process Dynamical Models. In: IEEE CVPR, pp. 238–245 (2006)Google Scholar
  23. 23.
    Urtasun, R., Fleet, D.J., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: ICCV, vol. 1, pp. 403–410 (2005)Google Scholar
  24. 24.
    Wang, J.M., Fleet, D.J., Hertzmann, A.: Gaussian Process Dynamical Models for Human Motion. IEEE PAMI 30(2), 283–298 (2008)CrossRefGoogle Scholar
  25. 25.
    Yao, A., Gal, J., Fanelli, G., van Gool, L.: Does human action recognition benefit from pose estimation? In: BMVC (2011)Google Scholar
  26. 26.
    Zhao, J., Badler, N.I.: Inverse kinematics positioning using nonlinear programming for highly articulated figures. ACM Transaction on Graphics 13(4), 313–336 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Søren Hauberg
    • 1
  • Kim Steenstrup Pedersen
    • 2
  1. 1.Perceiving SystemsMax Planck Institute for Intelligent SystemsTübingenGermany
  2. 2.Dept. of Computer ScienceUniversity of CopenhagenCopenhagenDenmark

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