Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation

An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models. The tracking appr...

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Vydáno v:	International journal of computer vision Ročník 87; číslo 1-2; s. 156 - 169
Hlavní autoři:	Corazza, Stefano, Mündermann, Lars, Gambaretto, Emiliano, Ferrigno, Giancarlo, Andriacchi, Thomas P.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Boston Springer US 01.03.2010 Springer Springer Nature B.V
Témata:	Algorithms Analysis Artificial Intelligence Cameras Computer animation Computer Imaging Computer programs Computer Science Datasets Degrees of freedom Image Processing and Computer Vision Mathematical analysis Mathematical models Motion capture Pattern Recognition Pattern Recognition and Graphics Segments Studies Surgical implants Toy industry Tracking Vision Tracking 3D reconstruction Human body model Shape from silhouette Markerless motion capture
ISSN:	0920-5691, 1573-1405
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Abstract	An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models. The tracking approach employed a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm with six degrees of freedom for each body segment. Anatomical consistency was maintained by enforcing rotational and translational joint range of motion constraints for each specific joint. A subject specific model of the subjects was obtained through an automatic model generation algorithm (Corazza et al. in IEEE Trans. Biomed. Eng., 2009 ) which combines a space of human shapes (Anguelov et al. in Proceedings SIGGRAPH, 2005 ) with biomechanically consistent kinematic models and a pose-shape matching algorithm. There were 15 anatomical body segments and 14 joints, each with six degrees of freedom (13 and 12, respectively for the HumanEva II dataset). The overall method is an improvement over (Mündermann et al. in Proceedings of CVPR, 2007 ) in terms of both accuracy and robustness. Since the method was originally developed for ≥8 cameras, the method performance was tested both (i) on the HumanEva II dataset (Sigal and Black, Technical Report CS-06-08, 2006 ) in a 4 camera configuration, (ii) on a series of motions including walking trials, a very challenging gymnastic motion and a dataset with motions similar to HumanEva II but with variable number of cameras.
AbstractList	An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models. The tracking approach employed a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm with six degrees of freedom for each body segment. Anatomical consistency was maintained by enforcing rotational and translational joint range of motion constraints for each specific joint. A subject specific model of the subjects was obtained through an automatic model generation algorithm (Corazza et al. in IEEE Trans. Biomed. Eng., 2009 ) which combines a space of human shapes (Anguelov et al. in Proceedings SIGGRAPH, 2005 ) with biomechanically consistent kinematic models and a pose-shape matching algorithm. There were 15 anatomical body segments and 14 joints, each with six degrees of freedom (13 and 12, respectively for the HumanEva II dataset). The overall method is an improvement over (Mündermann et al. in Proceedings of CVPR, 2007 ) in terms of both accuracy and robustness. Since the method was originally developed for ≥8 cameras, the method performance was tested both (i) on the HumanEva II dataset (Sigal and Black, Technical Report CS-06-08, 2006 ) in a 4 camera configuration, (ii) on a series of motions including walking trials, a very challenging gymnastic motion and a dataset with motions similar to HumanEva II but with variable number of cameras. An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models. The tracking approach employed a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm with six degrees of freedom for each body segment. Anatomical consistency was maintained by enforcing rotational and translational joint range of motion constraints for each specific joint. A subject specific model of the subjects was obtained through an automatic model generation algorithm (Corazza et al. in IEEE Trans. Biomed. Eng., 2009) which combines a space of human shapes (Anguelov et al. in Proceedings SIGGRAPH, 2005) with biomechanically consistent kinematic models and a pose-shape matching algorithm. There were 15 anatomical body segments and 14 joints, each with six degrees of freedom (13 and 12, respectively for the HumanEva II dataset). The overall method is an improvement over (Mundermann et al. in Proceedings of CVPR, 2007) in terms of both accuracy and robustness. Since the method was originally developed for [greater than or equal to]8 cameras, the method performance was tested both (i) on the HumanEva II dataset (Sigal and Black, Technical Report CS-06-08, 2006) in a 4 camera configuration, (ii) on a series of motions including walking trials, a very challenging gymnastic motion and a dataset with motions similar to HumanEva II but with variable number of cameras. Keywords Markerless motion capture * Tracking * 3D reconstruction * Human body model * Shape from silhouette An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models. The tracking approach employed a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm with six degrees of freedom for each body segment. Anatomical consistency was maintained by enforcing rotational and translational joint range of motion constraints for each specific joint. A subject specific model of the subjects was obtained through an automatic model generation algorithm (Corazza et al. in IEEE Trans. Biomed. Eng., 2009) which combines a space of human shapes (Anguelov et al. in Proceedings SIGGRAPH, 2005) with biomechanically consistent kinematic models and a pose-shape matching algorithm. There were 15 anatomical body segments and 14 joints, each with six degrees of freedom (13 and 12, respectively for the HumanEva II dataset). The overall method is an improvement over (Mundermann et al. in Proceedings of CVPR, 2007) in terms of both accuracy and robustness. Since the method was originally developed for >=8 cameras, the method performance was tested both (i) on the HumanEva II dataset (Sigal and Black, Technical Report CS-06-08, 2006) in a 4 camera configuration, (ii) on a series of motions including walking trials, a very challenging gymnastic motion and a dataset with motions similar to HumanEva II but with variable number of cameras. Issue Title: Special Issue: Evaluation of Articulated Human Motion and Pose Estimation An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models. The tracking approach employed a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm with six degrees of freedom for each body segment. Anatomical consistency was maintained by enforcing rotational and translational joint range of motion constraints for each specific joint. A subject specific model of the subjects was obtained through an automatic model generation algorithm (Corazza et al. in IEEE Trans. Biomed. Eng., 2009) which combines a space of human shapes (Anguelov et al. in Proceedings SIGGRAPH, 2005) with biomechanically consistent kinematic models and a pose-shape matching algorithm. There were 15 anatomical body segments and 14 joints, each with six degrees of freedom (13 and 12, respectively for the HumanEva II dataset). The overall method is an improvement over (Mündermann et al. in Proceedings of CVPR, 2007) in terms of both accuracy and robustness. Since the method was originally developed for ≥8 cameras, the method performance was tested both (i) on the HumanEva II dataset (Sigal and Black, Technical Report CS-06-08, 2006) in a 4 camera configuration, (ii) on a series of motions including walking trials, a very challenging gymnastic motion and a dataset with motions similar to HumanEva II but with variable number of cameras.[PUBLICATION ABSTRACT] An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models. The tracking approach employed a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm with six degrees of freedom for each body segment. Anatomical consistency was maintained by enforcing rotational and translational joint range of motion constraints for each specific joint. A subject specific model of the subjects was obtained through an automatic model generation algorithm (Corazza et al. in IEEE Trans. Biomed. Eng., 2009) which combines a space of human shapes (Anguelov et al. in Proceedings SIGGRAPH, 2005) with biomechanically consistent kinematic models and a pose-shape matching algorithm. There were 15 anatomical body segments and 14 joints, each with six degrees of freedom (13 and 12, respectively for the HumanEva II dataset). The overall method is an improvement over (Mundermann et al. in Proceedings of CVPR, 2007) in terms of both accuracy and robustness. Since the method was originally developed for [greater than or equal to]8 cameras, the method performance was tested both (i) on the HumanEva II dataset (Sigal and Black, Technical Report CS-06-08, 2006) in a 4 camera configuration, (ii) on a series of motions including walking trials, a very challenging gymnastic motion and a dataset with motions similar to HumanEva II but with variable number of cameras.
Audience	Academic
Author	Andriacchi, Thomas P. Mündermann, Lars Corazza, Stefano Ferrigno, Giancarlo Gambaretto, Emiliano
Author_xml	– sequence: 1 givenname: Stefano surname: Corazza fullname: Corazza, Stefano email: stefanoc@ccrma.stanford.edu organization: Stanford University – sequence: 2 givenname: Lars surname: Mündermann fullname: Mündermann, Lars organization: Stanford University – sequence: 3 givenname: Emiliano surname: Gambaretto fullname: Gambaretto, Emiliano organization: Politecnico di Milano – sequence: 4 givenname: Giancarlo surname: Ferrigno fullname: Ferrigno, Giancarlo organization: Politecnico di Milano – sequence: 5 givenname: Thomas P. surname: Andriacchi fullname: Andriacchi, Thomas P. organization: Stanford University, Bone and Joint RR&D, VA Palo Alto Hospital
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Publisher	Springer US Springer Springer Nature B.V
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Snippet	An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an... Issue Title: Special Issue: Evaluation of Articulated Human Motion and Pose Estimation An approach for accurately measuring human motion through Markerless...
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SubjectTerms	Algorithms Analysis Artificial Intelligence Cameras Computer animation Computer Imaging Computer programs Computer Science Datasets Degrees of freedom Image Processing and Computer Vision Mathematical analysis Mathematical models Motion capture Pattern Recognition Pattern Recognition and Graphics Segments Studies Surgical implants Toy industry Tracking Vision
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Title	Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation
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