Stick It! Articulated Tracking Using Spatial Rigid Object Priors

  • Søren Hauberg
  • Kim Steenstrup Pedersen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)


Articulated tracking of humans is a well-studied field, but most work has treated the humans as being independent of the environment. Recently, Kjellström et al. [1] showed how knowledge of interaction with a known rigid object provides constraints that lower the degrees of freedom in the model. While the phrased problem is interesting, the resulting algorithm is computationally too demanding to be of practical use. We present a simple and elegant model for describing this problem. The resulting algorithm is computationally much more efficient, while it at the same time produces superior results.


Joint Angle Hand Position Stereo Camera Rejection Sampling Optical Motion Capture System 
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.
    Kjellström, H., Kragić, D., Black, M.J.: Tracking people interacting with objects. In: CVPR 2010: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010)Google Scholar
  2. 2.
    Poppe, R.: Vision-based human motion analysis: An overview. Computer Vision and Image Understanding 108, 4–18 (2007)CrossRefGoogle Scholar
  3. 3.
    Capp, O., Godsill, S.J., Moulines, E.: An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE 95, 899–924 (2007)CrossRefGoogle Scholar
  4. 4.
    Nocedal, J., Wright, S.J.: Numerical optimization. Springer Series in Operations Research. Springer, Heidelberg (1999)CrossRefzbMATHGoogle Scholar
  5. 5.
    Erleben, K., Sporring, J., Henriksen, K., Dohlmann, H.: Physics Based Animation. Charles River Media, Hingham (2005)Google Scholar
  6. 6.
    Brubaker, M.A., Fleet, D.J., Hertzmann, A.: Physics-based person tracking using the anthropomorphic walker. International Journal of Computer Vision 87, 140–155 (2010)CrossRefGoogle Scholar
  7. 7.
    Wang, J.M., Fleet, D.J., Hertzmann, A.: Gaussian Process Dynamical Models for Human Motion. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 283–298 (2008)CrossRefGoogle Scholar
  8. 8.
    Sminchisescu, C., Jepson, A.: Generative modeling for continuous non-linearly embedded visual inference. In: ICML 2004: Proceedings of the Twenty-First International Conference on Machine Learning, pp. 759–766. ACM, New York (2004)Google Scholar
  9. 9.
    Lu, Z., Carreira-Perpinan, M., Sminchisescu, C.: People Tracking with the Laplacian Eigenmaps Latent Variable Model. In: Platt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems, vol. 20, pp. 1705–1712. MIT Press, Cambridge (2008)Google 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.
    Elgammal, A.M., Lee, C.S.: Tracking People on a Torus. IEEE Transaction on Pattern Analysis and Machine Intelligence 31, 520–538 (2009)CrossRefGoogle Scholar
  12. 12.
    Urtasun, R., Fleet, D.J., Fua, P.: 3D People Tracking with Gaussian Process Dynamical Models. In: CVPR 2006: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 238–245 (2006)Google Scholar
  13. 13.
    Urtasun, R., Fleet, D.J., Hertzmann, A., Fua, P.: Priors for people tracking from small training sets. In: Tenth IEEE International Conference on Computer Vision, vol. 1, pp. 403–410 (2005)Google Scholar
  14. 14.
    Bandouch, J., Engstler, F., Beetz, M.: Accurate human motion capture using an ergonomics-based anthropometric human model. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2008. LNCS, vol. 5098, pp. 248–258. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Balan, A.O., Sigal, L., Black, M.J.: A quantitative evaluation of video-based 3d person tracking. Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 349–356 (2005)CrossRefGoogle Scholar
  16. 16.
    Hauberg, S., Sommer, S., Pedersen, K.S.: Gaussian-like spatial priors for articulated tracking. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 425–437. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Yamamoto, M., Yagishita, K.: Scene constraints-aided tracking of human body. In: CVPR, pp. 151–156. IEEE Computer Society, Los Alamitos (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Søren Hauberg
    • 1
  • Kim Steenstrup Pedersen
    • 1
  1. 1.The eScience Centre, Dept. of Computer ScienceUniversity of CopenhagenDenmark

Personalised recommendations