Using Hierarchical Models for 3D Human Body-Part Tracking

  • Leonid Raskin
  • Michael Rudzsky
  • Ehud Rivlin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)


Human body pose estimation and tracking is a challenging task mainly because of the high dimensionality of the human body model. In this paper we introduce a Hierarchical Annealing Particle Filter (H-APF) algorithm for 3D articulated human body-part tracking. The method exploits Hierarchical Human Body Model (HHBM) in order to perform accurate body pose estimation. The method applies nonlinear dimensionality reduction combined with the dynamic motion model and the hierarchical body model. The dynamic motion model allows to make a better pose prediction, while the hierarchical model of the human body expresses conditional dependencies between the body parts and also allows us to capture properties of separate parts. The improved annealing approach is used for the propagation between different body models and sequential frames. The algorithm was checked on HumanEvaI and HumanEvaII datasets, as well as on other videos and proved to be effective and robust and was shown to be capable of performing an accurate and robust tracking. The comparison to other methods and the error calculations are provided.


Latent Space Hierarchical Model Locally Linear Embedding Human Body Model Nonlinear Dimensionality Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Lawrence, N.D., Moore, A.J.: Hierarchical gaussian process latent variable models. In: Proc. International Conference on Machine Learning (ICML) (2007)Google Scholar
  2. 2.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  3. 3.
    Elgammal, A.M., Lee, C.: Inferring 3D body pose from silhouettes using activity mani-fold learning. In: Proc. Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 681–688 (2004)Google Scholar
  4. 4.
    Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: Proc. Computer Vision and Pattern Recognition (CVPR), pp. 2126–2133 (2000)Google Scholar
  5. 5.
    Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision (IJCV) 29(1), 5–28 (1998)CrossRefGoogle Scholar
  6. 6.
    Sidenbladh, H., Black, M.J., Fleet, D.: Stochastic tracking of 3D human figures using 2D image motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Mikolajczyk, K., Schmid, K., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Rohr, K.: Human movement analysis based on explicit motion models. Motion-Based Recognition 8, 171–198 (1997)CrossRefzbMATHGoogle Scholar
  9. 9.
    Wang, Q., Xu, G., Ai, H.: Learning object intrinsic structure for robust visual tracking. In: Proc. Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 227–233 (2003)Google Scholar
  10. 10.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  11. 11.
    Urtasun, R., Fleet, D.J., Fua, P.: 3D people tracking with gaussian process dynamical models. In: Proc. Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 238–245 (2006)Google Scholar
  12. 12.
    Lawrence, N.D.: Gaussian process latent variable models for visualization of high dimensional data. In: Advances in Neural Information Processing Systems (NIPS), vol. 16, pp. 329–336 (2004)Google Scholar
  13. 13.
    Wang, J., Fleet, D.J., Hetzmann, A.: Gaussian process dynamical models. In: Information Processing Systems (NIPS), pp. 1441–1448 (2005)Google Scholar
  14. 14.
    Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: Proc. Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 1–8 (2008)Google Scholar
  15. 15.
    Raskin, L., Rudzsky, M., Rivlin, E.: Dimensionality reduction for articulated body tracking. In: Proc. The True Vision Capture, Transmission and Display of 3D Video (3DTV) (2007)Google Scholar
  16. 16.
    Balan, A., Sigal, L., Black, M.: A quantitative evaluation of video-based 3D person tracking. In: IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), pp. 349–356 (2005)Google Scholar
  17. 17.
    Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. International Journal of Computer Vision (IJCV) 61(2), 185–205 (2004)CrossRefGoogle Scholar
  18. 18.
    Sigal, L., Black, M.J.: Measure locally, reason globally: Occlusion-sensitive articulated pose estimation. In: Proc. Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 2041–2048 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Leonid Raskin
    • 1
  • Michael Rudzsky
    • 1
  • Ehud Rivlin
    • 1
  1. 1.Computer Science DepartmentTechnionTechnion City, HaifaIsrael

Personalised recommendations