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
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
Cardboard People: A Parameterized Model of Articulated Image Motion
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Skeleton-Based Motion Capture for Robust Reconstruction of Human Motion
CA '00 Proceedings of the Computer Animation
Estimation of the Location of Joint Points of Human Body from Successive Volume Data
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Human Motion Analysis: A Review
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
Motion estimation of elastic articulated objects from image points and contours
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Modeling of elastic articulated objects and its parameters determination from image contours
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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Motion can provide useful information about the structure of an articulated object. In this paper, we develop a method to estimate the revolute joints from two monocular images of the moving articulated object. Firstly, according to the characteristic of the articulated structure and motion, constraint equations about motion and joint parameters from image point correspondences are deduced, which provide crucial information for joint estimation. Then, the position ambiguity caused by the one DOF attribute, and the scale ambiguity resulted from monocular images are discussed. Also, a skillful function is employed to avoid the degeneration cases of estimation. Finally, the joint axis are computed from the estimated rotation matrices, and all estimated joint points are scaled to have the same scale factor to their ground truth. Simulations and experiments on real images show the correctness and efficiency of the algorithm.