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International Journal of Computer Vision

, Volume 61, Issue 2, pp 185–205 | Cite as

Articulated Body Motion Capture by Stochastic Search

  • Jonathan Deutscher
  • Ian Reid
Article

Abstract

We develop a modified particle filter which is shown to be effective at searching the high-dimensional configuration spaces (c. 30 + dimensions) encountered in visual tracking of articulated body motion. The algorithm uses a continuation principle, based on annealing, to introduce the influence of narrow peaks in the fitness function, gradually. The new algorithm, termed annealed particle filtering, is shown to be capable of recovering full articulated body motion efficiently. A mechanism for achieving a soft partitioning of the search space is described and implemented, and shown to improve the algorithm’s performance. Likewise, the introduction of a crossover operator is shown to improve the effectiveness of the search for kinematic trees (such as a human body). Results are given for a variety of agile motions such as walking, running and jumping.

human motion capture visual tracking particle filtering genetic algorithms 

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References

  1. Blake, A. and Isard, M. 1998. Active Contours. Springer.Google Scholar
  2. Cham, T.-J. and Rehg, J.M. 1999. Dynamic feature ordering for ef-ficient registration. In Proc. 7th Int’l Conf. on Computer Vision, Corfu, vol. 2, pp. 1084-1091.Google Scholar
  3. Cham, T.-J. and Rehg J.M. 1999. A multiple hypothesis approach to figure tracking. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 239-245.Google Scholar
  4. Deutscher, J., Blake, A., North, B., and Bascle, B. 1999. Tracking through singularities and discontinuities by random sampling. In Proc. 7th Int. Conf. on Computer Vision, vol. 2, pp. 1144-1149.Google Scholar
  5. Deutscheer, Blake, A., and Reid, I.D. 2000. Articulated body motion capture by annealed particle filtering. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 126-133.Google Scholar
  6. Deutscher, J., Davison, A.J., and Reid, I.D. 2001. Automatic parti-tioning of high dimensional search spaces associated with articu-lated body motion capture. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 669-676.Google Scholar
  7. Deutscher, J., Isard, M., and MacCormick, J. 2002. Automatic cam-era calibration from a single manhattan image. In Proc. 7th Euro-pean Conf. on Computer Vision, Copenhagen, vol. 4, pp. 175-188.Google Scholar
  8. Drummond, T. and Cipolla, R. 2001. Real-time tracking of highly articulated suctures in the presence of noisy measurements. In Proc. 8th Int’l Conf. on Computer Vision, Vancouver, pp. 315-320.Google Scholar
  9. Gavrila, D. and Davis, L.S. 1996. 3d model-based tracking of humans in action: A multi-view approach. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 73-80.Google Scholar
  10. Harris, C.G. 1992. Tracking with rigid models. In Active Vision. A. Blake and A. Yuille (Eds.), MIT Press: Cambridge, MA.Google Scholar
  11. Isard, M.A. and Blake, A. 1996. Visual tracking by stochastic prop-agation of conditional density. In Proc. 4th European Conf. on Computer Vision, Cambridge, England, p. 343-356.Google Scholar
  12. Kirkpatrick, S., Gellatt, C.D., and Vecchi, M.P. 1983. Optimisation by simulated annealing. Science, 220(4598):671-680.Google Scholar
  13. Lyons, D. 2002. A qualitative approach to computer sign language recognition. Master’s thesis, University of Oxford.Google Scholar
  14. MacCormick, J. 2000. Probabilistic models and stochastic algorithms for visual hacking. PhD thesis, University of Oxford.Google Scholar
  15. MacCormick, J. and Blake, A. 1999. A probabilistic exclusion prin-ciple for tracking multiple objects. In Proc. 7th Int. Conf. on Com-purter Vision, vol. 1, pp. 572-578.Google Scholar
  16. MacCormick, J. and Isaru-d, M. 2000. Partitioned sampling, articulated objects and interface-quality hand hacking. In Proc. 6th European Conf. on Computer Vision, Dublin, vol. 2, pp. 3-19.Google Scholar
  17. Metropolis, N., Rosenbluth, AW., Rosenbluth, M.N., Teller, A.H., and Teller, E. 1953. Equations of state calculations by fast corm-putting machine. J. Chern. Phys., 21:1087-1091.CrossRefGoogle Scholar
  18. Mikic, I., Trivedi, M., hunter, E., and Cosman, P. 2001. Articulated body posture estimation from multi-camera voxel data. In Proc. International Journal of Computer Vision KL3179-05/5384380 of the IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 455-462.Google Scholar
  19. Neal, R.M. 2001. Annealed importance sampling. Statistics and Computing, ( 11):125-139.CrossRefMathSciNetGoogle Scholar
  20. Plankers, R. and Fua, P. 2003. Articulated soft objects for multi-view shape and motion capture. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10).Google Scholar
  21. Sidenbladh, H., Black, M.J., and Fleet, D.J. 2000. Stochastic track-ing of 3D human figures using 2D image motion. In Proc. 6th European Conf. on Computer Vision, Dublin, vol. 2, pp. 702-718.Google Scholar
  22. Sidenbladh, H., Black, M.J., and Sigal, L. 2002. Implicit probabilistic models of human motion for synthesis and tracking. In Proc. 7th European Conf.i on Computer Vision, Copenhagen, vol. 1, pp. 784-800.Google Scholar
  23. Sminchisescu, C. and Triggs, B. 2001. Covariance scaled sampling for monocular 3d body tracking. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 447-454.Google Scholar
  24. Sminchisescu, C. and Triggs, B. 2002. Hyperdynamics importance sampling. In Proc. 7th European Conf. on Computer Vision, Copenhagen, vol. 1, pp. 769-783.Google Scholar
  25. Sminchisescu, C. and Triggs, B. 2003. Kinematic jump processes for monocular 3d human tracking. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 69-76.Google Scholar
  26. Sullivan, J., Blake, A., Isard, M., and MacConnick, J. 1999. Object localization by bayesian correlation. In Proc. 7th Int. Conf. on Computer Vision, vol. 2, pp. 1068-1075.Google Scholar
  27. Wachter, S. and Nagel, IH. 1999. Tracking persons in monocular image sequences. Computer Vision and Image Understanding, 74(3):174-192.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2005

Authors and Affiliations

  • Jonathan Deutscher
    • 1
  • Ian Reid
    • 1
  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordUnited Kingdom

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