Swapping-Based Partitioned Sampling for Better Complex Density Estimation: Application to Articulated Object Tracking

  • Séverine Dubuisson
  • Christophe Gonzales
  • Xuan Son Nguyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6929)

Abstract

In this paper, we propose to better estimate high-dimensional distributions by exploiting conditional independences within the Particle Filter (PF) framework. We first exploit Dynamic Bayesian Networks to determine conditionally independent subspaces of the state space, which allows us to independently perform propagations and corrections over smaller spaces. Second, we propose a swapping process to transform the weighted particle set provided by the update step of PF into a “new particle set” better focusing on high peaks of the posterior distribution. This new methodology, called Swapping-Based Partitioned Sampling, is successfully tested and validated for articulated object tracking.

Keywords

State Space Posterior Distribution Tracking Error Particle Filter Conditional Independence 
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 2011

Authors and Affiliations

  • Séverine Dubuisson
    • 1
  • Christophe Gonzales
    • 1
  • Xuan Son Nguyen
    • 1
  1. 1.Laboratoire d’Informatique de Paris 6 (LIP6/UPMC)ParisFrance

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