Simultaneous Partitioned Sampling for Articulated Object Tracking

  • Christophe Gonzales
  • Séverine Dubuisson
  • Xuan Son N’Guyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6915)


In this paper, we improve the Partitioned Sampling (PS) scheme to better handle high-dimensional state spaces. PS can be explained in terms of conditional independences between random variables of states and observations. These can be modeled by Dynamic Bayesian Networks. We propose to exploit these networks to determine conditionally independent subspaces of the state space. This allows us to simultaneously perform propagations and corrections over smaller spaces. This results in reducing the number of necessary resampling steps and, in addition, in focusing particles into high-likelihood areas. This new methodology, called Simultaneous Partitioned Sampling, is successfully tested and validated for articulated object tracking.


Particle Filter Conditional Independence Dynamic Bayesian Network Sequential Monte Carlo Shade Rectangle 
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.


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  1. 1.
    Bernier, O., Cheungmonchan, P., Bouguet, A.: Fast nonparametric belief propagation for real-time stereo articulated body tracking. Computer Vision and Image Understanding 113(1), 29–47 (2009)CrossRefGoogle Scholar
  2. 2.
    Bray, M., Koller-Meier, E., Müller, P., Schraudolph, N.N., Van Gool, L.: Stochastic optimization for high-dimensional tracking in dense range maps. IEE Proceedings Vision, Image and Signal Processing 152(4), 501–512 (2005)CrossRefGoogle Scholar
  3. 3.
    Chang, W.Y., Chen, C.S., Jian, Y.D.: Visual tracking in high-dimensional state space by appearance-guided particle filtering. IEEE Transactions on Image Processing 17(7), 1154–1167 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chen, Z.: Bayesian filtering: from kalman filters to particle filters, and beyond (2003)Google Scholar
  5. 5.
    Deutscher, J., Davison, A., Reid, I.: Automatic partitioning of high dimensional search spaces associated with articulated body motion capture. In: CVPR, vol. 2, pp. 669–676 (2005)Google Scholar
  6. 6.
    Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-gaussian bayesian state estimation. IEE Proceedings of Radar and Signal Processing 140(2), 107–113 (1993)CrossRefGoogle Scholar
  7. 7.
    Hauberg, S., Sommer, S., Pedersen, K.: 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
  8. 8.
    Hauberg, S., Pedersen, K.S.: Stick it! articulated tracking using spatial rigid object priors. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 758–769. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28 (1998)CrossRefGoogle Scholar
  10. 10.
    MacCormick, J.: Probabilistic modelling and stochastic algorithms for visual localisation and tracking. Ph.D. thesis, Oxford University (2000)Google Scholar
  11. 11.
    MacCormick, J., Blake, A.: A probabilistic exclusion principle for tracking multiple objects. In: ICCV, pp. 572–587 (1999)Google Scholar
  12. 12.
    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
  13. 13.
    Murphy, K.: Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, UC Berkeley, Computer Science Division (2002)Google Scholar
  14. 14.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman Publishers, Inc., San Francisco (1988)zbMATHGoogle Scholar
  15. 15.
    Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Qu, W., Schonfeld, D.: Real-time decentralized articulated motion analysis and object tracking from videos. IEEE Transactions on Image Processing 16(8), 2129–2138 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rose, C., Saboune, J., Charpillet, F.: Reducing particle filtering complexity for 3D motion capture using dynamic bayesian networks. In: AAAI, pp. 1396–1401 (2008)Google Scholar
  18. 18.
    Sánchez, A., Pantrigo, J., Gianikellis, K.: Combining Particle Filter and Population-based Metaheuristics for Visual Articulated Motion Tracking. Electronic Letters on Computer Vision and Image Analysis 5(3), 68–83 (2005)Google Scholar
  19. 19.
    Shen, C., van den Hengel, A., Dick, A., Brooks, M.: 2D articulated tracking with dynamic Bayesian networks. In: Das, G., Gulati, V.P. (eds.) CIT 2004. LNCS, vol. 3356, pp. 130–136. Springer, Heidelberg (2004)Google Scholar
  20. 20.
    Smith, K., Gatica-perez, D.: Order matters: a distributed sampling method for multi-object tracking. In: BMVC, pp. 25–32 (2004)Google Scholar
  21. 21.
    Widynski, N., Dubuisson, S., Bloch, I.: Introducing fuzzy spatial constraints in a ranked partitioned sampling for multi-object tracking. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammoud, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6453, pp. 393–404. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christophe Gonzales
    • 1
  • Séverine Dubuisson
    • 1
  • Xuan Son N’Guyen
    • 1
  1. 1.Laboratoire d’Informatique de Paris 6 (LIP6/UPMC)ParisFrance

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