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)


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.


Motion Capture Kinematic Model Motion Capture System Rigid Object Rigid Link 
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  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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