A mixed-state Condensation tracker with automatic
model-switching
Michael Isard and Andrew Blake
Proc 6th Int. Conf. Computer Vision, 107-112 (1998).
Abstract
There is considerable interest in the computer vision community in
representing and modelling motion. Motion models are used as
predictors to increase the robustness and accuracy of visual trackers,
and as classifiers for gesture recognition. This paper presents a
significant development of random sampling methods to allow automatic
switching between multiple motion models as a natural extension of the
tracking process. The Bayesian mixed-state framework is described in
its generality, and the example of a bouncing ball is used to
demonstrate that a mixed-state model can significantly improve
tracking performance in heavy clutter. The relevance of the approach
to the problem of gesture recognition is then investigated using a
tracker which is able to follow the natural drawing action of a hand
holding a pen, and switches state according to the hand's motion.
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