CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Using Interval Particle Filtering for Marker Less 3D Human Motion Capture
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
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
Adaptive parallel/serial sampling mechanisms for particle filtering in dynamic Bayesian networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Simultaneous partitioned sampling for articulated object tracking
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
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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 (DBN) 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.