International Journal of Computer Vision

, Volume 29, Issue 1, pp 5–28

CONDENSATION—Conditional Density Propagation for Visual Tracking

Authors

  • Michael Isard
    • Department of Engineering ScienceUniversity of Oxford
  • Andrew Blake
    • Department of Engineering ScienceUniversity of Oxford
Article

DOI: 10.1023/A:1008078328650

Cite this article as:
Isard, M. & Blake, A. International Journal of Computer Vision (1998) 29: 5. doi:10.1023/A:1008078328650

Abstract

The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.

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Copyright information

© Kluwer Academic Publishers 1998