Learning to track the visual motion of contours
Andrew Blake, Michael Isard and David Reynard
Artificial Intelligence, 78, 101--133. (1995)
Abstract
A development of a method for tracking visual contours is described.
Given an ``un-trained'' tracker, a training-motion of an object can
be observed over some extended time and stored as an image sequence.
The image sequence is used to learn parameters in a stochastic
differential equation model. These are used, in turn, to build a
tracker whose predictor imitates the motion in the training set.
Tests show that the resulting trackers can be markedly tuned to
desired curve shapes and classes of motions.
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