Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Nonparametric belief propagation for self-calibration in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Fast nonparametric belief propagation for real-time stereo articulated body tracking
Computer Vision and Image Understanding
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
Nonparametric belief propagation
Communications of the ACM
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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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.