Reconstruction of articulated objects from point correspondences in a single uncalibrated image
Computer Vision and Image Understanding
Human Body Model Acquisition and Tracking Using Voxel Data
International Journal of Computer Vision
Singularity Analysis for Articulated Object Tracking
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Twist Based Acquisition and Tracking of Animal and Human Kinematics
International Journal of Computer Vision
Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Markerless tracking of complex human motions from multiple views
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Markerless monocular motion capture using image features and physical constraints
CGI '05 Proceedings of the Computer Graphics International 2005
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
A new approach for body pose recovery
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
Hi-index | 0.00 |
A human pose estimation method from monocular image captures is presented. The objective is to develop a human-computer interface (HCI) for virtual sport activities. In the proposed technique, a graphical 3D human model is first constructed. Its projection on a virtual image plane is then used to match the silhouettes obtained from the image sequence. By iteratively adjusting the 3D pose of the graphical 3D model with the physical and anatomic constraints of human motion, the human pose and the associate 3D motion parameters can be uniquely identified. Experimental results are presented with the real scene images.