CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Implicit Probabilistic Models of Human Motion for Synthesis and 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
Human Body Model Acquisition and Motion Capture Using Voxel Data
AMDO '02 Proceedings of the Second International Workshop on Articulated Motion and Deformable Objects
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face Recognition from Video: A CONDENSATION Approach
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Mode-based Multi-Hypothesis Head Tracking Using Parametric Contours
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Head Tracking by Active Particle Filtering
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Modelling the 3D pose of a human arm and the shoulder complex utilising only two parameters
Integrated Computer-Aided Engineering
HCI Beyond the GUI: Design for Haptic, Speech, Olfactory, and Other Nontraditional Interfaces
HCI Beyond the GUI: Design for Haptic, Speech, Olfactory, and Other Nontraditional Interfaces
Representation and matching of articulated shapes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In recent years Sequential Monte Carlo (SMC) methodshave been applied to handle some of the problems inherentto model-based tracking. In this paper two issues regardingSMC are investigated in the context of estimating the3D pose of the human arm. Firstly, we investigate how toapply a sub-space to representing the pose of a human armmore efficiently, i.e., reducing the dimensionality. Secondly,we investigate how to apply a local method to estimated themaximum a posteriori (MAP). The former issue is based oncombining a screw axis representation with the position ofthe hand in the image. The latter issue is handled by applyinga method based on maximising a proximity function, toestimate the MAP. We find that both the sub-space and theproximity function are sound strategies and that they are animprovement over the current SMC-methods.