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
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
3D human body model acquisition from multiple views
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Novel skeletal representation for articulated creatures
Novel skeletal representation for articulated creatures
Recovering articulated object models from 3D range data
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Articulated models from video
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
On bending invariant signatures for surfaces
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
Multicamera tracking of articulated human motion using shape and motion cues
IEEE Transactions on Image Processing
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Motion capture is an important application in different areas such as biomechanics, computer animation, and human-computer interaction. Current motion capture methods typically use human body models in order to guide pose estimation and tracking. We model the human body as a set of tapered super-quadrics connected in an articulated structure and propose an algorithm to automatically estimate the parameters of the model using video sequences obtained from multiple calibrated cameras. Our method is based on the fact that the human body is constructed of several articulated chains that can be visualised as essentially 1-D segments embedded in 3-D space and connected at specific joint locations. The proposed method first computes a voxel representation from the images and maps the voxels to a high dimensional space in order to extract the 1-D structure. A bottom-up approach is then suggested in order to build a parametric (spline-based) representation of a general articulated body in the high dimensional space followed by a top-down probabilistic approach that registers the segments to the known human body model. We then present an algorithm to estimate the parameters of our model using the segmented and registered voxels