Machine Learning
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace
IEEE Transactions on Pattern Analysis and Machine Intelligence
A system for marker-less human motion estimation
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Human skeleton tracking from depth data using geodesic distances and optical flow
Image and Vision Computing
A data-driven approach for real-time full body pose reconstruction from a depth camera
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Real-time human pose tracking from range data
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Part template: 3D representation for multiview human pose estimation
Pattern Recognition
A survey of human motion analysis using depth imagery
Pattern Recognition Letters
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Human pose estimation in real-time is a challenging problem in computer vision. In this paper, we present a novel approach to recover a 3D human pose in real-time from a single depth human silhouette using Principal Direction Analysis (PDA) on each recognized body part. In our work, the human body parts are first recognized from a depth human body silhouette via the trained Random Forests (RFs). On each recognized body part which is presented as a set of 3D points cloud, PDA is applied to estimate the principal direction of the body part. Finally, a 3D human pose gets recovered by mapping the principal directional vector to each body part of a 3D human body model which is created with a set of super-quadrics linked by the kinematic chains. In our experiments, we have performed quantitative and qualitative evaluations of the proposed 3D human pose reconstruction methodology. Our evaluation results show that the proposed approach performs reliably on a sequence of unconstrained poses and achieves an average reconstruction error of 7.46 degree in a few key joint angles. Our 3D pose recovery methodology should be applicable to many areas such as human computer interactions and human activity recognition.