Skip to main content
Book cover

European Conference on Computer Vision

ECCV 2000: Computer Vision — ECCV 2000 pp 150–166Cite as

Tracking Discontinuous Motion Using Bayesian Inference

  • Conference paper

    • We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

  • First Online:

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1843)

Abstract

Robustly tracking people in visual scenes is an important task for surveillance, human-computer interfaces and visually mediated interaction. Existing attempts at tracking a person’s head and hands deal with ambiguity, uncertainty and noise by intrinsically assuming a consistently continuous visual stream and/or exploiting depth information. We present a method for tracking the head and hands of a human subject from a single view with no constraints on the continuity of motion. Hence the tracker is appropriate for real-time applications in which the availability of visual data is constrained, and motion is discontinuous. Rather than relying on spatio-temporal continuity and complex 3D models of the human body, a Bayesian Belief Network deduces the body part positions by fusing colour, motion and coarse intensity measurements with contextual semantics.

Keywords

  • Bayesian Inference
  • Hand Position
  • Gesture Recognition
  • Belief Revision
  • Hand Orientation

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.

Download conference paper PDF

References

  1. Y. Azoz, L. Devi, and R. Sharma. Tracking hand dynamics in unconstrained environments. In Proc. 3rd IEEE Int. Conf. on Automatic Face and Gesture Recognition, pages 247–279, Japan, 1998.

    Google Scholar 

  2. Andrew Blake and Michael Isard. Active Contours. Springer-Verlag, 1998.

    Google Scholar 

  3. Gary R. Bradski. Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal, 2nd Quarter, 1998.

    Google Scholar 

  4. Hilary Buxton and Shaogang Gong. Visual surveillance in a dynamic and uncertain world. Artificial Intelligence, 78:431–459, 1995.

    CrossRef  Google Scholar 

  5. T. Cham and J. Rehg. Dynamic feature ordering for efficient registration. In IEEE Int. Conf. on Computer Vision, volume 2, pages 1084–1091, Greece, Sept. 1999.

    Google Scholar 

  6. E. Charniak. Bayesian networks without tears. AI Magazine, 12(4):50–63, 1991.

    Google Scholar 

  7. James J. Clark and Alan L. Yuille. Data Fusion for Sensory Information Processing Systems. Kluwer Academic Publishers, 1990.

    Google Scholar 

  8. Robert G. Cowell, A. Philip Dawid, Steffen L. Lauritzen, and David J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer-Verlag, NY, 1999.

    MATH  Google Scholar 

  9. D. M. Gavrila. The visual analysis of human movement-a survey. Computer Vision and Image Understanding, 73(1), 1999.

    Google Scholar 

  10. D.M. Gavrila and L.S. Davis. 3-D model-based tracking of human motion in action. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 73–80, 1996.

    Google Scholar 

  11. Kazuyuki Imagawa, Shan Lu, and Seiji Igi. Color-based hands tracking system for sign language recognition. In Proc. 3rd IEEE Int. Conf. on Automatic Face and Gesture Recognition, pages 462–467, Nara, Japan, 1998.

    Google Scholar 

  12. M. Isard and A. Blake. CONDENSATION-conditional density propagation for visual tracking. International Journal of Computer Vision, 29(1):5–28, 1998.

    CrossRef  Google Scholar 

  13. C. Jennings. Robust finger tracking with multiple cameras. In Proc. of Int. Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-Time Systems, pages 152–160, Corfu, Greece, September 1999. IEEE Computer Society.

    Google Scholar 

  14. N. Jojic, M. Turk, and T. Huang. Tracking self-occluding articulated objects in dense disparity maps. In IEEE Int. Conf. on Computer Vision, volume 1, pages 123–130, Greece, Sept. 1999.

    Google Scholar 

  15. S. Lauritzen and D. Spiegelhalter. Local computations with probabilities on graphical structures and their application to expert systems. In G. Shafer and J. Pearl, editors, Readings in Uncertain Reasoning, pages 415–448. Morgan Kauffmann, 1990.

    Google Scholar 

  16. J. Martin, V. Devin, and J. Crowley. Active hand tracking. In Proc. 3rd IEEE Int. Conf. on Automatic Face and Gesture Recognition, pages 573–578, Japan, 1998.

    Google Scholar 

  17. D. Metaxas. Deformable model and HMM-based tracking, analysis and recognition of gestures and faces. In Proc. of Int. Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-Time Systems, pages 136–140, Greece, Sept. 1999. IEEE Computer Society.

    Google Scholar 

  18. Darnell J. Moore, Irfan A. Essa, and Monson H. Hayes III. Exploiting human actions and object context for recognition tasks. In IEEE International Conference on Computer Vision, volume 1, pages 80–86, Corfu, Greece, September 1999.

    Google Scholar 

  19. Eng-Jon Ong and Shaogang Gong. A dynamic human model using hybrid 2D-3D representations in hierarchical PCA space. In British Machine Vision Conference, volume 1, pages 33–42, Nottingham, UK, September 1999. BMVA.

    Google Scholar 

  20. E. Osuna, R. Freund, and F. Girosi. Training support vector machines: An application to face detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 130–136, 1997.

    Google Scholar 

  21. Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA, 1988.

    MATH  Google Scholar 

  22. Y. Raja, S. J. McKenna, and S. Gong. Tracking and segmenting people in varying lighting conditions using colour. In Proc. 3rd IEEE Int. Conf. on Automatic Face and Gesture Recognition, pages 228–233, Nara, Japan, 1998.

    Google Scholar 

  23. Herbert Simon. Rational choice and the structure of the environment. Psychological Review, 63:129–138, 1956.

    CrossRef  Google Scholar 

  24. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland. Pfinder: Real-time tracking of the human body. IEEE Trans. on PAMI, 19(7):780–785, July 1997.

    CrossRef  Google Scholar 

  25. C. Wren and A. Pentland. Understanding purposeful human motion. In IEEE Int. Workshop on Modelling People, pages 19–25, Corfu, Greece, September 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Queen Mary and Westfield College, London, E1 4NS, UK

    Jamie Sherrah

Authors
  1. Jamie Sherrah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaogang Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Glenageary, Co., 5 Edwin Court, Dublin, Ireland

    David Vernon

Rights and permissions

Reprints and Permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sherrah, J., Gong, S. (2000). Tracking Discontinuous Motion Using Bayesian Inference. In: Vernon, D. (eds) Computer Vision — ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45053-X_10

Download citation

  • DOI: https://doi.org/10.1007/3-540-45053-X_10

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67686-7

  • Online ISBN: 978-3-540-45053-5

  • eBook Packages: Springer Book Archive

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.