Static and Dynamic Human Shape Modeling
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
Multicamera tracking of articulated human motion using shape and motion cues
IEEE Transactions on Image Processing
Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition
Foundations and Trends in Signal Processing
Segmentation of human body parts using deformable triangulation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Computer Vision and Image Understanding
Detection human motion with heel strikes for surveillance analysis
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Multiview human pose estimation with unconstrained motions
Pattern Recognition Letters
Human action recognition using multiple views: a comparative perspective on recent developments
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Real-time pose estimation using constrained dynamics
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Part template: 3D representation for multiview human pose estimation
Pattern Recognition
Heel strike detection based on human walking movement for surveillance analysis
Pattern Recognition Letters
Principal direction analysis-based real-time 3D human pose reconstruction from a single depth image
Proceedings of the Fourth Symposium on Information and Communication Technology
Comparing evolutionary algorithms and particle filters for Markerless Human Motion Capture
Applied Soft Computing
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We propose a general approach using Laplacian Eigenmaps and a graphical model of the human body to segment 3D voxel data of humans into different articulated chains. In the bottom-up stage, the voxels are transformed into a high-dimensional (6D or less) Laplacian Eigenspace (LE) of the voxel neighborhood graph. We show that LE is effective at mapping voxels on long articulated chains to nodes on smooth 1D curves that can be easily discriminated, and prove these properties using representative graphs. We fit 1D splines to voxels belonging to different articulated chains such as the limbs, head and trunk, and determine the boundary between splines using the spline fitting error. A top-down probabilistic approach is then used to register the segmented chains, utilizing their mutual connectivity and individual properties. Our approach enables us to deal with complex poses such as those where the limbs form loops. We use the segmentation results to automatically estimate the human body models. While we use human subjects in our experiments, the method is fairly general and can be applied to voxel-based segmentation of any articulated object composed of long chains. We present results on real and synthetic data that illustrate the usefulness of this approach.