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Real-Time Automatic Kinematic Model Building for Optical Motion Capture Using a Markov Random Field

  • Stjepan Rajko
  • Gang Qian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4796)

Abstract

We present a completely autonomous algorithm for the real-time creation of a moving subject’s kinematic model from optical motion capture data and with no a priori information. Our approach solves marker tracking, the building of the kinematic model, and the tracking of the body simultaneously. The novelty lies in doing so through a unifying Markov random field framework, which allows the kinematic model to be built incrementally and in real-time. We validate the potential of this method through experiments in which the system is able to accurately track the movement of the human body without an a priori model, as well as through experiments on synthetic data.

Keywords

Motion Capture Kinematic Model Motion Capture System Rigid Object Rigid Link 
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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References

  1. 1.
    Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81(3), 231–268 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Yan, J., Pollefeys, M.: Automatic kinematic chain building from feature trajectories of articulated objects. In: IEEE CVPR, IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  3. 3.
    Silaghi, M.C., Plankers, R., Boulic, R., Fua, P., Thalmann, D.: Local and global skeleton fitting techniques for optical motion capture. IFIP CapTech  (1998)Google Scholar
  4. 4.
    Ringer, M., Lasenby, J.: A procedure for automatically estimating model parameters in optical motion capture. BMVC  (2002)Google Scholar
  5. 5.
    Zakotnik, J., Matheson, T., Durr, V.: A posture optimization algorithm for model-based motion capture of movement sequences. Journal of Neuroscience Methods  (2004)Google Scholar
  6. 6.
    Karaulova, I.A., Hall, P.M., Marshall, A.D.: A hierarchical model of dynamics for tracking people with a single video camera. In: BMVC, Bristol, England (2000)Google Scholar
  7. 7.
    Veenman, C.J., Reinders, M.J.T., Backer, E.: Resolving motion correspondence for densely moving points. IEEE Transactions on Pattern Analysis and Machine Intelligence  (2001)Google Scholar
  8. 8.
    Shafique, K., Shah, M.: A non-iterative greedy algorithm for multi-frame point correspondence. In: Int. Conf. Computer Vision, Nice, France, pp. 110–115 (2003)Google Scholar
  9. 9.
    Hornung, A., Sar-Dessai, S., Kobbelt, L.: Self-calibrating optical motion tracking for articulated bodies. In: IEEE Virtual Reality Conference (2005)Google Scholar
  10. 10.
    Rajko, S., Qian, G.: Autonomous real-time model building for optical motion capture. In: IEEE ICIP, pp. 1284–1287. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Stjepan Rajko
    • 1
  • Gang Qian
    • 1
  1. 1.Arts, Media and Engineering Program, Arizona State University, Tempe AZ 85287USA

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