The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
Model-Based Estimation of 3D Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking persons in monocular image sequences
Computer Vision and Image Understanding
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
Human Body Model Acquisition and Tracking Using Voxel Data
International Journal of Computer Vision
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
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)
Singularity Analysis for Articulated Object Tracking
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Incremental Tracking of Human Actions from Multiple Views
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multiple Cues used in Model-Based Human Motion Capture
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
3D Articulated Models and Multi-View Tracking with Silhouettes
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Constraining Human Body Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Combining 3D flow fields with silhouette-based human motion capture for immersive video
Graphical Models - Special issue on pacific graphics 2003
A convenient multicamera self-calibration for virtual environments
Presence: Teleoperators and Virtual Environments
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Active-Vision System Reconfiguration for Form Recognition in the Presence of Dynamic Obstacles
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Vision-Based Motion Capture of Interacting Multiple People
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Multicamera tracking of articulated human motion using shape and motion cues
IEEE Transactions on Image Processing
3D hand tracking in a stochastic approximation setting
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Hi-index | 0.00 |
We present a framework and algorithm for tracking articulated motion for humans. We use multiple calibrated cameras and an articulated human shape model. Tracking is performed using motion cues as well as image-based cues (such as silhouettes and “motion residues” hereafter referred to as spatial cues,) as opposed to constructing a 3D volume image or visual hulls. Our algorithm consists of a predictor and corrector: the predictor estimates the pose at the t + 1 using motion information between images at t and t + 1. The error in the estimated pose is then corrected using spatial cues from images at t + 1. In our predictor, we use robust multi-scale parametric optimisation to estimate the pixel displacement for each body segment. We then use an iterative procedure to estimate the change in pose from the pixel displacement of points on the individual body segments. We present a method for fusing information from different spatial cues such as silhouettes and “motion residues” into a single energy function. We then express this energy function in terms of the pose parameters, and find the optimum pose for which the energy is minimised.