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Data-Driven Importance Distributions for Articulated Tracking

  • Søren Hauberg
  • Kim Steenstrup Pedersen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6819)

Abstract

We present two data-driven importance distributions for particle filter-based articulated tracking; one based on background subtraction, another on depth information. In order to keep the algorithms efficient, we represent human poses in terms of spatial joint positions. To ensure constant bone lengths, the joint positions are confined to a non-linear representation manifold embedded in a high-dimensional Euclidean space. We define the importance distributions in the embedding space and project them onto the representation manifold. The resulting importance distributions are used in a particle filter, where they improve both accuracy and efficiency of the tracker. In fact, they triple the effective number of samples compared to the most commonly used importance distribution at little extra computational cost.

Keywords

Articulated tracking Importance Distributions Particle Filtering Spatial Human Motion Models 

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References

  1. 1.
    Poppe, R.: Vision-based human motion analysis: An overview. Computer Vision and Image Understanding 108, 4–18 (2007)CrossRefGoogle Scholar
  2. 2.
    Sminchisescu, C., Triggs, B.: Kinematic Jump Processes for Monocular 3D Human Tracking. In: In IEEE International Conference on Computer Vision and Pattern Recognition, pp. 69–76 (2003)Google Scholar
  3. 3.
    Duetscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: CVPR, pp. 21–26. IEEE Computer Society, Los Alamitos (2000)Google Scholar
  4. 4.
    Sminchisescu, C., Triggs, B.: Estimating articulated human motion with covariance scaled sampling. The International Journal of Robotics Research 22, 371 (2003)CrossRefGoogle Scholar
  5. 5.
    Kjellström, H., Kragić, D., Black, M.J.: Tracking people interacting with objects. In: IEEE CVPR (2010)Google Scholar
  6. 6.
    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
  7. 7.
    Urtasun, R., Fleet, D.J., Fua, P.: 3D People Tracking with Gaussian Process Dynamical Models. In: IEEE CVPR, pp. 238–245 (2006)Google Scholar
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    Doucet, A., Godsill, S., Andrieu, C.: On sequential monte carlo sampling methods for bayesian filtering. Statistics and computing 10, 197–208 (2000)CrossRefGoogle Scholar
  13. 13.
    Erleben, K., Sporring, J., Henriksen, K., Dohlmann, H.: Physics Based Animation. Charles River Media (2005)Google Scholar
  14. 14.
    Balan, A.O., Sigal, L., Black, M.J., Davis, J.E., Haussecker, H.W.: Detailed human shape and pose from images. In: IEEE CVPR, pp. 1–8 (2007)Google Scholar
  15. 15.
    Vondrak, M., Sigal, L., Jenkins, O.C.: Physical simulation for probabilistic motion tracking. In: CVPR. IEEE Computer Society Press, Los Alamitos (2008)Google Scholar
  16. 16.
    Gall, J., Rosenhahn, B., Brox, T., Seidel, H.-P.: Optimization and filtering for human motion capture. International Journal of Computer Vision 87, 75–92 (2010)CrossRefGoogle Scholar
  17. 17.
    Bandouch, J., Beetz, M.: Tracking humans interacting with the environment using efficient hierarchical sampling and layered observation models. In: Computer Vision Workshops, ICCV Workshops (2009)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    Wang, J.M., Fleet, D.J., Hertzmann, A.: Gaussian Process Dynamical Models for Human Motion. IEEE PAMI 30, 283–298 (2008)CrossRefGoogle Scholar
  20. 20.
    Lu, Z., Carreira-Perpinan, M., Sminchisescu, C.: People Tracking with the Laplacian Eigenmaps Latent Variable Model. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems, vol. 20, pp. 1705–1712. MIT Press, Cambridge (2008)Google Scholar
  21. 21.
    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
  22. 22.
    Hauberg, S., Sloth, J.: An efficient algorithm for modelling duration in hidden markov models, with a dramatic application. J. Math. Imaging Vis. 31, 165–170 (2008)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  24. 24.
    Ziegler, J., Nickel, K., Stiefelhagen, R.: Tracking of the articulated upper body on multi-view stereo image sequences. In: IEEE CVPR, pp. 774–781 (2006)Google Scholar
  25. 25.
    Ganapathi, V., Plagemann, C., Koller, D., Thrun, S.: Real time motion capture using a single time-of-flight camera. In: IEEE CVPR, pp. 755–762 (2010)Google Scholar
  26. 26.
    Arya, S., Mount, D.M.: Approximate nearest neighbor queries in fixed dimensions. In: Proc. 4th ACM-SIAM Sympos. Discrete Algorithms, pp. 271–280 (1993)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

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