Approximate nearest neighbor queries in fixed dimensions
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Hybrid Monte Carlo Filtering: Edge-Based People Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Physics-based Animation (Graphics Series)
Physics-based Animation (Graphics Series)
Tracking of the Articulated Upper Body on Multi-View Stereo Image Sequences
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application
Journal of Mathematical Imaging and Vision
Accurate Human Motion Capture Using an Ergonomics-Based Anthropometric Human Model
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
Physics-Based Person Tracking Using the Anthropomorphic Walker
International Journal of Computer Vision
Gaussian-like spatial priors for articulated tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Stick it articulated tracking using spatial rigid object priors
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
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We present two data-driven importance distributions for particle filterbased articulated tracking; one based on background subtraction, another on depth information. In order to keep the algorithms efficient, we represent human poses in terms of spatial joint positions. To ensure constant bone lengths, the joint positions are confined to a non-linear representation manifold embedded in a high-dimensional Euclidean space. We define the importance distributions in the embedding space and project them onto the representation manifold. The resulting importance distributions are used in a particle filter, where they improve both accuracy and efficiency of the tracker. In fact, they triple the effective number of samples compared to the most commonly used importance distribution at little extra computational cost.