International Journal of Robotics Research
Retargetting motion to new characters
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Reconstruction of articulated objects from point correspondences in a single uncalibrated image
Computer Vision and Image Understanding
Estimating Human Body Configurations Using Shape Context Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Model-based tracking of self-occluding articulated objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Inferring 3D Structure with a Statistical Image-Based Shape Model
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning silhouette features for control of human motion
ACM Transactions on Graphics (TOG)
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
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering 3D Human Body Configurations Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Conditional Random People: Tracking Humans with CRFs and Grid Filters
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
Constrained optimization for human pose estimation from depth sequences
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Proposal maps driven MCMC for estimating human body pose in static images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Kinematic jump processes for monocular 3D human tracking
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
Editorial: Special issue on Time-of-Flight camera based computer vision
Computer Vision and Image Understanding
Multiple people tracking and pose estimation with occlusion estimation
Computer Vision and Image Understanding
Human typical action recognition using gray scale image of silhouette sequence
Computers and Electrical Engineering
Performance capture of interacting characters with handheld kinects
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Computer Methods and Programs in Biomedicine
A method of abnormal habits recognition in intelligent space
Engineering Applications of Artificial Intelligence
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This paper presents a model-based, Cartesian control theoretic approach for estimating human pose from a set of key features points (key-points) detected using depth images obtained from a time-of-flight imaging device. The key-points represent positions of anatomical landmarks, detected and tracked over time based on a probabilistic inferencing algorithm that is robust to partial occlusions and capable of resolving ambiguities in detection. The detected key-points are subsequently kinematically self retargeted, or mapped to the subject's own kinematic model, in order to predict the pose of an articulated human model at the current state, resolve ambiguities in key-point detection, and provide estimates of missing or intermittently occluded key-points. Based on a standard kinematic and mesh model of a human, constraints such as joint limit avoidance, and self-penetration avoidance are enforced within the retargeting framework. Effectiveness of the algorithm is demonstrated experimentally for upper and full-body pose reconstruction from a small set of detected key-points. On average, the proposed algorithm runs at approximately 10 frames per second for the upper-body and 5 frames per second for whole body reconstruction on a standard 2.13GHz laptop PC.