Distance-ordered homotopic thinning: a skeletonization algorithm for 3D digital images
Computer Vision and Image Understanding
A parallel 3D 12-subiteration thinning algorithm
Graphical Models and Image Processing
Self-Organizing Maps
Hierarchical 3D Pose Estimation for Articulated Human Body Models from a Sequence of Volume Data
RobVis '01 Proceedings of the International Workshop on Robot Vision
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Fusion of 2d and 3d sensor data for articulated body tracking
Robotics and Autonomous Systems
Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
Deictic gestures with a time-of-flight camera
GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
Learning to interpret pointing gestures with a time-of-flight camera
Proceedings of the 6th international conference on Human-robot interaction
A multimedia presentation system using a 3D gesture interface in museums
Multimedia Tools and Applications
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We describe a technique for estimating human pose from an image sequence captured by a time-of-flight camera. The pose estimation is derived from a simple model of the human body that we fit to the data in 3D space. The model is represented by a graph consisting of 44 vertices for the upper torso, head, and arms. The anatomy of these body parts is encoded by the edges, i.e. an arm is represented by a chain of pairwise connected vertices whereas the torso consists of a 2-dimensional grid. The model can easily be extended to the representation of legs by adding further chains of pairwise connected vertices to the lower torso. The model is fit to the data in 3D space by employing an iterative update rule common to self-organizing maps. Despite the simplicity of the model, it captures the human pose robustly and can thus be used for tracking the major body parts, such as arms, hands, and head. The accuracy of the tracking is around 5---6 cm root mean square (RMS) for the head and shoulders and around 2 cm RMS for the head. The implementation of the procedure is straightforward and real-time capable.