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Reducing Particle Filtering Complexity for 3D Motion Capture using Dynamic Bayesian Networks

Cédric Rose 1, 2 Jamal Saboune 1 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Particle filtering algorithms can be used for the monitoring of dynamic systems with continuous state variables and without any constraints on the form of the probability distributions. The dimensionality of the problem remains a limitation of these approaches due to the growing number of particles required for the exploration of the state space. Computer vision problems such as 3D motion tracking are an example of complex monitoring problems which have a high dimensional state space and observation functions with high computational cost. In this article we focus on reducing the required number of particles in the case of monitoring tasks where the state vector and the observation function can be factored. We introduce a particle filtering algorithm based on the dynamic Bayesian network formalism which takes advantage of a factored representation of the state space for efficiently weighting and selecting the particles. We illustrate the approach on a simulated and a realworld 3D motion tracking tasks.
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Submitted on : Tuesday, October 21, 2008 - 3:15:51 PM
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Cédric Rose, Jamal Saboune, François Charpillet. Reducing Particle Filtering Complexity for 3D Motion Capture using Dynamic Bayesian Networks. Twenty-Third Conference on Artificial Intelligence - AAAI-08, Jul 2008, Chicago, United States. ⟨inria-00332714⟩

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