Advertisement

A smoothing filter for condensation

  • Michael Isard
  • Andrew Blake
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1406)

Abstract

Condensation, recently introduced in the computer vision literature, is a particle filtering algorithm which represents a tracked object's state using an entire probability distribution. Clutter can cause the distribution to split temporarily into multiple peaks, each representing a different hypothesis about the object configuration. When measurements become unambiguous again, all but one peak, corresponding to the true object position, die out. While several peaks persist estimating the object position is problematic. “Smoothing” in this context is the statistical technique of conditioning the state distribution on both past and future measurements once tracking is complete. After smoothing, peaks corresponding to clutter are reduced, since their trajectories eventually die out. The result can be a much improved state-estimate during ambiguous time-steps. This paper implements two algorithms to smooth the output of a Condensation filter. The techniques are derived from the work of Kitagawa, reinterpreted in the Condensation framework, and considerably simplified.

Keywords

State Density Object Position Smoothing Algorithm Factor Sampling Object Configuration 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    A. Gelb, editor. Applied Optimal Estimation. MIT Press, Cambridge, MA, 1974.Google Scholar
  2. 2.
    N. Gordon, D. Salmond, and A.F.M. Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. F, 140(2):107–113, 1993.Google Scholar
  3. 3.
    U. Grenander, Y. Chow, and D.M. Keenan. HANDS. A Pattern Theoretical Study of Biological Shapes. Springer-Verlag. New York, 1991.Google Scholar
  4. 4.
    T. Heap and D. Hogg. Wormholes in shape space: Tracking through discontinuous changes in shape. In Proc. 6th Int. Conf. on Computer Vision, 1998.Google Scholar
  5. 5.
    M.A. Isard and A. Blake. Visual tracking by stochastic propagation of conditional density. In Proc. 4th European Conf. Computer Vision, 343–356, Cambridge, England, Apr 1996.Google Scholar
  6. 6.
    M.A. Isard and A. Blake. A mixed-state Condensation tracker with automatic model switching. In Proc. 6th Int. Conf. on Computer Vision, 107–112, 1998.Google Scholar
  7. 7.
    G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1):1–25, 1996.MathSciNetCrossRefGoogle Scholar
  8. 8.
    L. Rabiner and J. Bing-Hwang. Fundamentals of speech recognition. Prentice-Hall, 1993.Google Scholar
  9. 9.
    B.D. Ripley. Stochastic simulation. New York: Wiley, 1987.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Michael Isard
    • 1
  • Andrew Blake
    • 1
  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordUK

Personalised recommendations