Contour tracking by stochastic propagation of conditional
density
Michael Isard and Andrew Blake
Proc. European Conference on Computer Vision, vol. 1, pp. 343--356,
Cambridge UK, (1996).
Abstract
The problem of tracking curves in dense visual clutter is a
challenging one. Trackers based on Kalman filters are of limited use;
because they are based on Gaussian densities which are unimodal, they
cannot represent simultaneous alternative hypotheses. Extensions to
the Kalman filter to handle multiple data associations work
satisfactorily in the simple case of point targets, but do not extend
naturally to continuous curves. A new, stochastic algorithm is
proposed here, the Condensation algorithm --- Conditional
Density Propagation over time. It uses `factored sampling', a method
previously applied to interpretation of static images, in which the
distribution of possible interpretations is represented by a randomly
generated set of representatives. The Condensation algorithm
combines factored sampling with learned dynamical models to propagate
an entire probability distribution for object position and shape, over
time. The result is highly robust tracking of agile motion in
clutter, markedly superior to what has previously been attainable from
Kalman filtering. Notwithstanding the use of stochastic methods, the
algorithm runs in near real-time.
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