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Tracking deforming objects using particle filtering for geometric active contours.

Yogesh Rathi1, Namrata Vaswani, Allen Tannenbaum

  • 1School of Electrical and Computer Engineering, Georgia Institute of Technology, VL E392B, Atlanta, GA 30332, USA. Yogesh.rathi@gatech.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 15, 2007
PubMed
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This study introduces a novel particle filtering algorithm within the geometric active contour framework for tracking deforming objects. This method offers a robust solution for analyzing complex object motion and shape changes over time.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Object tracking is crucial for analyzing dynamic scenes.
  • Existing methods like Kalman and particle filters struggle with parametrization and topological changes.
  • Geometric active contours offer a robust, parametrization-independent alternative.

Purpose of the Study:

  • To develop a particle filtering algorithm for tracking deforming objects.
  • To address limitations of existing tracking methods in handling complex shape dynamics.
  • To implement tracking on a theoretically infinite-dimensional state space.

Main Methods:

  • Formulation of a particle filtering algorithm within the geometric active contour framework.
  • Utilizing geometric active contours for parametrization independence and topological change handling.

Related Experiment Videos

  • Application to tracking moving and deforming objects.

    • Main Results:

    • Successful implementation of a particle filtering algorithm for deforming object tracking.
    • Demonstrated ability to handle changes in curve topology.
    • First known implementation of approximate particle filtering for tracking on an infinite-dimensional state space.

    Conclusions:

    • The proposed method provides a powerful tool for tracking deforming objects.
    • Geometric active contours combined with particle filtering offer significant advantages over traditional methods.
    • This research opens new avenues for real-time analysis of dynamic shapes.