A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Robust Position Tracking for Mobile Robots with Adaptive Evolutionary Particle Filter
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Reducing particle filtering complexity for 3D motion capture using dynamic Bayesian networks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Monitoring the variables of real world dynamical systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task, yielding probability distribution over the unobserved variables. However, they suffer from the curse of dimensionality problem: the necessary number of particles grows exponentially with the dimensionality of the hidden state space. The problem is aggravated when the initial distribution of the variables is not well known, as happens in global localization problems. In this paper we present two new adaptive sampling mechanisms for PFs for systems whose variable dependencies can be factored into a Dynamic Bayesian Network. The novel PFs, developed over the proposed sampling mechanisms, exploit the strengths of other existing PFs. Their adaptive mechanisms 1) modify or establish probabilistic links among the subspaces of hidden variables that are independently explored to build particles consistent with the current measurements and past history, and 2) tune the performance of the new PFs toward the behaviors of several existing PFs. We demonstrate their performance on some complex dynamical system estimation problems, showing that our methods successfully localize and track hidden states, and outperform some of the existing PFs.