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CONDENSATION -- conditional density propagation for visual tracking (1998)

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by Michael Isard , Andrew Blake
Citations:1503 - 12 self
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BibTeX

@MISC{Isard98condensation--,
    author = {Michael Isard and Andrew Blake},
    title = {CONDENSATION -- conditional density propagation for visual tracking},
    year = {1998}
}

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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 unimodal, 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.

Keyphrases

kluwer academic publisher    condensation conditional density propagation    visual tracking    stochastic method    visual clutter    condensation algorithm    factored sampling    substantial clutter    dynamical model    control theory    robust tracking    computer vision    stochastic framework    dense visual clutter    foreground object    visual observation    gaussian density    sampling algorithm    probability distribution    agile motion    static image    possible interpretation    simultaneous alternative hypothesis    kalman filtering   

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