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Conditional filters for image sequence-based tracking--application to point tracking.

Elise Arnaud1, Etienne Mémin, Bruno Cernuschi-Frías

  • 1IRISA, Université de Rennes 1, Rennes, France. Elise.Arnaud@irisa.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 14, 2005
PubMed
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This paper introduces novel conditional filters for image sequence tracking. These advanced filters enhance point tracking accuracy, even with noisy data and complex motion.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Classical filtering methods often struggle with complex image sequence analysis.
  • Estimating system states and measurements from image data presents significant challenges in tracking applications.

Purpose of the Study:

  • To propose a new conditional formulation of classical filtering methods for image sequence-based tracking.
  • To develop advanced point trackers capable of handling challenging tracking scenarios.

Main Methods:

  • A novel conditional filter formulation is developed.
  • Two point trackers are derived: a linear tracker using a conditional linear minimum variance estimator and a nonlinear tracker using a conditional particle filter.
  • The model integrates optical flow constraints with matching-based measurements for point tracking.

Related Experiment Videos

Main Results:

  • The linear tracker effectively handles image sequences with global-dominant motion.
  • The nonlinear tracker accurately tracks points with locally described motion.
  • Conditional trackers demonstrate significant improvements in general scenarios, including noisy sequences, abrupt trajectory changes, occlusions, and cluttered backgrounds.

Conclusions:

  • The proposed conditional filtering approach offers a robust solution for image sequence tracking.
  • These advanced trackers enhance tracking performance and reliability in diverse and challenging real-world conditions.