The problem of tracking curves in dense visual clutter is challenging. Kalman \ufb01ltering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses \u201cfactored sampling\u201d, 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
The problem of tracking curves in dense visual clutter is challenging. Kalman \ufb01ltering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. 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