A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Recovering articulated object models from 3D range data
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Avoiding the "Streetlight Effect": Tracking by Exploring Likelihood Modes
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Tracking of the Articulated Upper Body on Multi-View Stereo Image Sequences
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
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
Nonlinear body pose estimation from depth images
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Kinematic self retargeting: A framework for human pose estimation
Computer Vision and Image Understanding
Bayesian 3d human body pose tracking from depth image sequences
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Real-time human pose recognition in parts from single depth images
Communications of the ACM
A survey of human motion analysis using depth imagery
Pattern Recognition Letters
An adaptable system for RGB-D based human body detection and pose estimation
Journal of Visual Communication and Image Representation
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A new 2-step method is presented for human upper-body pose estimation from depth sequences, in which coarse human part labeling takes place first, followed by more precise joint position estimation as the second phase. In the first step, a number of constraints are extracted from notable image features such as the head and torso. The problem of pose estimation is cast as that of label assignment with these constraints. Major parts of the human upper body are labeled by this process. The second step estimates joint positions optimally based on kinematic constraints using dense correspondences between depth profile and human model parts. The proposed framework is shown to overcome some issues of existing approaches for human pose tracking using similar types of data streams. Performance comparison with motion capture data is presented to demonstrate the accuracy of our approach.