Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Physics-based Animation (Graphics Series)
Physics-based Animation (Graphics Series)
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
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
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Predicting Articulated Human Motion from Spatial Processes
International Journal of Computer Vision
Data-driven importance distributions for articulated tracking
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Simultaneous partitioned sampling for articulated object tracking
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Natural metrics and least-committed priors for articulated tracking
Image and Vision Computing
Unscented Kalman Filtering on Riemannian Manifolds
Journal of Mathematical Imaging and Vision
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Articulated tracking of humans is a well-studied field, but most work has treated the humans as being independent of the environment. Recently, Kjellström et al. [1] showed how knowledge of interaction with a known rigid object provides constraints that lower the degrees of freedom in the model. While the phrased problem is interesting, the resulting algorithm is computationally too demanding to be of practical use. We present a simple and elegant model for describing this problem. The resulting algorithm is computationally much more efficient, while it at the same time produces superior results