Learning to track curves in motion
Andrew Blake, Michael Isard and David Reynard.
Proc. IEEE Int. Conf. Decision Theory and Control}, 3788--3793. (1994)
Abstract
Recent developments in video-tracking allow the outlines of moving,
natural objects in a video-camera input stream to be tracked live, at
full video-rate. The system used here is based on Kalman Filtering
with a B-spline representation of curves to track the silhouettes of
moving non-polyhedral objects. For example hands, lips, legs,
vehicles, fruit can be tracked at video-rate without any special
hardware beyond a desktop workstation and a video-camera and
framestore.
The novel contribution of this paper is a tracking algorithm that uses
a bootstrapping technique to learn a stochastic, dynamic model
for given motions from example video-streams. Incorporating such a
model into the tracking algorithm greatly enhances maximum tracking
speed and robustness to distraction from background objects.
Experiments with learning both rigid and non-rigid motions, using
moving hands and lips, clearly show the increased tracking power
resulting from the learned dynamics.
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