Learning to track curves in motion

A. Blake, M.A. Isard, and D. Reynard

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.

Details

Publication typeInproceedings
Published inProc. IEEE Int. Conf. Decision Theory and Control
Pages3788–3793
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