Tracking a Person with 3-D Motion by Integrating Optical Flow and Depth
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Markerless tracking of complex human motions from multiple views
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Fuzzy Sets and Systems
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SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
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Computers and Graphics
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Image and Vision Computing
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
Computer Methods and Programs in Biomedicine
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Human pose estimation from depth image using visibility estimation and key points
DHM'13 Proceedings of the 4th international conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management: human body modeling and ergonomics - Volume Part II
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Pattern Recognition Letters
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Proceedings of the Fourth Symposium on Information and Communication Technology
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Computer Methods and Programs in Biomedicine
Real-time Hand Gesture Recognition from Depth Images Using Convex Shape Decomposition Method
Journal of Signal Processing Systems
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In this paper, we present a method for human full-body pose estimation from depth data that can be obtained using Time of Flight (ToF) cameras or the Kinect device. Our approach consists of robustly detecting anatomical landmarks in the 3D data and fitting a skeleton body model using constrained inverse kinematics. Instead of relying on appearance-based features for interest point detection that can vary strongly with illumination and pose changes, we build upon a graph-based representation of the depth data that allows us to measure geodesic distances between body parts. As these distances do not change with body movement, we are able to localize anatomical landmarks independent of pose. For differentiation of body parts that occlude each other, we employ motion information, obtained from the optical flow between subsequent intensity images. We provide a qualitative and quantitative evaluation of our pose tracking method on ToF and Kinect sequences containing movements of varying complexity.