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
3D Skeleton-Based Body Pose Recovery
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Real-time 3-D human body tracking using learnt models of behaviour
Computer Vision and Image Understanding
Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Human Motion with Multiple Cameras Using an Articulated Model
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
The i3DPost Multi-View and 3D Human Action/Interaction Database
CVMP '09 Proceedings of the 2009 Conference for Visual Media Production
Real-time and markerless 3D human motion capture using multiple views
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
3D reconstruction of human motion and skeleton from uncalibrated monocular video
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
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Pose estimation in the context of human motion analysis is the process of approximating the body configuration in each frame of a motion sequence. We propose a novel pose estimation method based on fitting a skeletal model to tree structures built from skeletonised visual hulls reconstructed from multi-view video. The pose is estimated independently in each frame, hence the method can recover from errors in previous frames, which overcomes some problems of tracking. Publically available datasets were used to evaluate the method. On real data the method performs at a framerate of $\sim\!14$ fps. Using synthetic data the positions of the joints were determined with a mean error of $\sim\!6$ cm.