2D Articulated Body Tracking with Self-occultations Handling

  • Eric Para
  • Olivier Bernier
  • Catherine Achard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5098)


Recently many methods for human articulated body tracking were proposed in the literature. These techniques are often computationally intensive and cannot be used for Human-Computer Interface. We propose in this article a real-time algorithm for upper body tracking with occultation handling. The tracking is based on an articulated body model, also used to automatically initialize the target. After an independent search of the most likely positions of each limb, a dynamic programming algorithm is used to find the best configuration according to the links between limbs. The self-occultations between the limbs are directly taken into account by the tracking algorithm and results show the interest of the proposed approach.


Tracking humanoid articulated model occultations handling dynamic programming real-time processing computer vision 


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  1. 1.
    Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(2), 90–126 (2006)CrossRefGoogle Scholar
  2. 2.
    Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-time surveillance of people and their actions. IEEE Trans. Pattern Anal. Mach. Intell. 22, 809–830 (2000)CrossRefGoogle Scholar
  3. 3.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial Structures for Object Recognition. Int. J. Comput. Vision 61(1), 55–79 (2005)CrossRefGoogle Scholar
  4. 4.
    Ramanan, D., Forsyth, D.A., Zisserman, A.: Tracking People by Learning Their Appearance. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 65–81 (2007)CrossRefGoogle Scholar
  5. 5.
    Ju, S.X., Black, M.J., Yacoob, Y.: Cardboard People: A Parameterized Model of Articulated Image Motion. In: Int. Conf. on Auto. Face and Gesture Recognition, pp. 38–44 (1996)Google Scholar
  6. 6.
    Demirdjian, D., Taycher, L., Shakhnarovich, G., Grauman, K., Darrell, T.: Avoiding the Streetlight Effect: Tracking by Exploring Likelihood Modes. In: IEEE Int. Conf. on Comput. Vis., vol. 1, pp. 357–364 (2005)Google Scholar
  7. 7.
    Sigal, L., Black, M.J.: Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation. IEEE Comput. Society Conf. on Comput. Vis. and Pattern Recognition 2, 2041–2048 (2006)Google Scholar
  8. 8.
    Fischler, M.A., Elschlager, R.A.: The representation and matching of pictorial structures. IEEE Transactions on Computer 22, 67–92 (1973)CrossRefGoogle Scholar
  9. 9.
    Bernier, O., Cheung-Mon-Chan, P.: Real-Time 3D Articulated Pose Tracking using Particle Filtering and Belief Propagation on Factor Graphs. In: British Machine Vision Conf. (2006)Google Scholar
  10. 10.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient matching of pictorial structures. Comput. Vis. and Pattern Recognition 2, 66–73 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Eric Para
    • 1
  • Olivier Bernier
    • 1
  • Catherine Achard
    • 2
  1. 1.Orange Labs, France Telecom R&DTechnopole AnticipaLannionFrance
  2. 2.Université Pierre et Marie CurieParisFrance

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