Posture Constraints for Bayesian Human Motion Tracking

  • Ignasi Rius
  • Javier Varona
  • Xavier Roca
  • Jordi González
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4069)


One of the most used techniques for full-body human tracking consists of estimating the probability of the parameters of a human body model over time by means of a particle filter. However, given the high-dimensionality of the models to be tracked, the number of required particles to properly populate the space of solutions makes the problem computationally very expensive. To overcome this, we present an efficient scheme which makes use of an action-specific model of human postures to guide the prediction step of the particle filter, so only feasible human postures are considered. As a result, the prediction step of this model-based tracking approach samples from a first order motion model only those postures which are accepted by our action-specific model. In this manner, particles are propagated to locations in the search space with most a posteriori information avoiding particle wastage. We show that this scheme improves the efficiency and accuracy of the overall tracking approach.


Action Model Human Motion Human Posture Prediction Step Tracking Approach 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chai, J., Hodgins, J.K.: Performance animation from low-dimensional control signals. SIGGRAPH 2005, ACM Trans. Graph. 24(3), 686–696 (2005)Google Scholar
  2. 2.
    Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. IJCV 61(2), 185–205 (2005)CrossRefGoogle Scholar
  3. 3.
    Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)MATHGoogle Scholar
  4. 4.
    Gonzàlez, J.: Human Sequence Evaluation: the Key-frame Approach. PhD thesis, Universitat Autònoma de Barcelona (2004)Google Scholar
  5. 5.
    González, J., Varona, J., Roca, F.X., Villanueva, J.J.: Analysis of human walking based on aSpaces. In: Perales, F.J., Draper, B.A. (eds.) AMDO 2004. LNCS, vol. 3179, pp. 177–188. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Horprasert, T., Harwood, D., Davis, L.S.: A robust background subtraction and shadow detection. In: Proc. Asian. Conf. on Comp. Vision (January 2000)Google Scholar
  7. 7.
    MacCormick, J., Isard, M.: Partitioned sampling, articulated objects, and interface-quality hand tracking. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 3–19. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Ning, H., Tan, T., Wang, L., Hu, W.: Kinematics-based tracking of human walking in monocular video sequences. IVC 22, 429–441 (2004)Google Scholar
  9. 9.
    Rius, I., Rowe, D., González, J., Roca, F.X.: A 3D dynamic model of human actions for probabilistic image tracking. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 529–536. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Sidenbladh, H., Black, M.J., Fleet, D.J.: 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
  11. 11.
    Sidenbladh, H., Black, M.J., Sigal, L.: Implicit probabilistic models of human motion for synthesis and tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 784–800. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Stadler, W.: Analytical Robotics and Mechatronics. McGraw-Hill, NY, USA (1995)Google Scholar
  13. 13.
    Urtasun, R., Fleet, D.J., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: IEEE International Conference on Computer Vision (ICCV 2005), vol. 1, pp. 403–410 (2005)Google Scholar
  14. 14.
    Wachter, S., Nagel, H.H.: Tracking persons in monocular image sequences. CVIU 74(3), 174–192 (1999)Google Scholar
  15. 15.
    Wu, Y., Lin, J., Huang, T.S.: Analyzing and capturing articulated hand motion in image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(12), 1910–1922 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ignasi Rius
    • 1
  • Javier Varona
    • 2
  • Xavier Roca
    • 1
  • Jordi González
    • 3
  1. 1.Centre de Visió per ComputadorBellaterraSpain
  2. 2.Unitat de Gráfics i Visió per ComputadorPalma de MallorcaSpain
  3. 3.UPC St. LLorens i ArtigasInstitut de Robòtica i Informática IndBarcelonaSpain

Personalised recommendations