Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
2D Articulated Tracking with Dynamic Bayesian Networks
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
Fast nonparametric belief propagation for real-time stereo articulated body tracking
Computer Vision and Image Understanding
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
Gaussian-like spatial priors for articulated tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Introducing fuzzy spatial constraints in a ranked partitioned sampling for multi-object tracking
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Stick it articulated tracking using spatial rigid object priors
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Real-Time Decentralized Articulated Motion Analysis and Object Tracking From Videos
IEEE Transactions on Image Processing
Visual Tracking in High-Dimensional State Space by Appearance-Guided Particle Filtering
IEEE Transactions on Image Processing
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In this paper, we improve the Partitioned Sampling (PS) scheme to better handle high-dimensional state spaces. PS can be explained in terms of conditional independences between random variables of states and observations. These can be modeled by Dynamic Bayesian Networks. We propose to exploit these networks to determine conditionally independent subspaces of the state space. This allows us to simultaneously perform propagations and corrections over smaller spaces. This results in reducing the number of necessary resampling steps and, in addition, in focusing particles into high-likelihood areas. This new methodology, called Simultaneous Partitioned Sampling, is successfully tested and validated for articulated object tracking.