Particle Filtering with Dynamic Shape Priors

  • Yogesh Rathi
  • Samuel Dambreville
  • Allen Tannenbaum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)


Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a locally linear embedding in order to incorporate dynamic shape information into the particle filtering framework for tracking highly deformable objects in the presence of noise and clutter.


State Space Model Shape Information Locally Linear Embedding Rigid Transformation Signed Distance Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yogesh Rathi
    • 1
  • Samuel Dambreville
    • 1
  • Allen Tannenbaum
    • 1
  1. 1.Georgia Institute of TechnologyAtlanta

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