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Contour Tracking By Stochastic Propagation of Conditional Density (1996)
| Citations: | 660 - 23 self |
BibTeX
@INPROCEEDINGS{Isard96contourtracking,
author = {Michael Isard and Andrew Blake},
title = {Contour Tracking By Stochastic Propagation of Conditional Density},
booktitle = {},
year = {1996},
pages = {343--356}
}
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Abstract
. In Proc. European Conf. Computer Vision, 1996, pp. 343--356, Cambridge, UK 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 pos...
Keyphrases
stochastic propagation contour tracking conditional density kalman filter entire probability distribution multiple data association stochastic algorithm condensation algorithm combine factored sampling limited use represent simultaneous alternative hypothesis european conf learned dynamical model computer vision continuous curve point target dense visual clutter conditional density propagation object po gaussian density static image possible interpretation simple case
