CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Approximations for decision making in the Dempster-Shafer theory of evidence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Particle filtering in the Dempster--Shafer theory
International Journal of Approximate Reasoning
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Particle filtering has come into favor in the computer vision community with the CONDENSATION algorithm. Perhaps the main reason for this is that it relaxes many of the assumptions made with other tracking algorithms, such as the Kalman filter. It still places a strong requirement on the ability to model the observations and dynamics of the systems with conditional probabilities. In practice these may be hard to measure precisely, especially in situations where multiple sensors are used.Here, a particle filtering algorithm which uses evidential reasoning is presented, which relaxes the need to be able to precisely model observations, and also provides an explicit model of ignorance.