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