A Mixed-State Condensation Tracker with Automatic Model-Switching

  • Authors:
  • Michael Isard;Andrew Blake

  • Affiliations:
  • -;-

  • Venue:
  • ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
  • Year:
  • 1998

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Abstract

There is considerable interest in the computer vision community in representing and modelling motion. Motion models are use d 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.