Synonyms
Motion recovery 3D; Video-based motion capture
Definition
Markerless human motion capture from images entails recovering the successive 3D poses of a human body moving in front of one or more cameras, which should be achieved without additional sensors or markers to be worn by the person. The 3D poses are usually expressed in terms of the joint angles of a kinematic model including an articulated skeleton and volumetric primitives designed to approximate the body shape. They can be used to analyze, modify, and resynthesize the motion. As no two people move in exactly the same way, they also constitute a signature that can be used for identification purposes.
Introduction
Understanding and recording human and other vertebrate motion from images is a long-standing interest. In its modern form, it goes back at least to Eadweard Muybridge [1] and Etienne-Jules Marey [2] in the nineteenth century. They can be considered as the precursors of human motion and animal locomotion...
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References
E. Muybridge, Animals Locomotion (University of Pennsylvania, Philadelphia, 1887)
E.J. Marey, Le Mouvement (Editions Jaqueline Chambon, 1994). Réédition de 1894 des éditions Masson
L. Muendermann, S. Corazza, T. Andriachhi, The evolution of methods for the capture of human movement leading to markerless motion capture for biomedical applications. J. NeuroEng. Rehabil. 3, 6 (2006)
T. Moeslund, A. Hilton, V. Krueger, A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 2, 90–126 (2006)
J. Deutscher, A. Blake, I. Reid, Articulated body motion capture by annealed particle filtering, in Conference on Computer Vision and Pattern Recognition, Hilton Head Island, 2000, pp. 2126–2133
D. Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers, J. Davis, Scape: shape completion and animation of people. ACM Trans. Graph. 24, 408–416 (2005)
E. Seemann, B. Leibe, B. Schiele, Multi-aspect detection of articulated objects, in Conference on Computer Vision and Pattern Recognition, New York, 2006
D. Ramanan, A. Forsyth, A. Zisserman, Tracking people by learning their appearance. IEEE Trans. Pattern Anal. Mach. Intell. 29, 65–81 (2007)
A. Fossati, M. Dimitrijevic, V. Lepetit, P. Fua, Bridging the gap between detection and tracking for 3D monocular video-based motion capture, in Conference on Computer Vision and Pattern Recognition, Minneapolis, 2007
H. Murase, R. Sakai, Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recognit. Lett. 17, 155–162 (1996)
R. Urtasun, D. Fleet, A. Hertzman, P. Fua, Priors for people tracking from small training sets, in International Conference on Computer Vision, Beijing, 2005
D. Ormoneit, H. Sidenbladh, M. Black, T. Hastie, Learning and tracking cyclic human motion, in Neural Information Processing Systems, Vancouver, 2001, pp. 894–900
H. Sidenbladh, M.J. Black, D.J. Fleet, Stochastic tracking of 3D human figures using 2D image motion, in European Conference on Computer Vision, Dublin, 2000
N. Troje, Decomposing biological motion: a framework for analysis and synthesis of human gait patterns. J. Vis. 2, 371–387 (2002)
Q. He, C. Debrunner, Individual recognition from periodic activity using Hidden Markov Models, in IEEE Workshop on Human Motion, Austin, 2000
S. Niyogi, E.H. Adelson, Analyzing and recognizing walking figures in XYT, in Conference on Computer Vision and Pattern Recognition, Seattle, 1994
J. Little, J. Boyd, Recognizing people by their gait: the shape of motion. Videre 1, 1–32 (1986)
C.Y. Yam, M.S. Nixon, J.N. Carter, On the relationship of human walking and running: automatic person identification by gait, in International Conference on Pattern Recognition, Quebec, 2002, pp. 287–290
D. Cunado, M. Nixon, J. Carter, Automatic extraction and description of human gait models for recognition purposes. Comput. Vis. Image Underst. 90, 1–41 (2003)
R. Urtasun, D. Fleet, P. Fua, Temporal motion models for monocular and multiview 3-D human body tracking. Comput. Vis. Image Underst. 104, 157–177 (2006)
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Fua, P. (2015). Markerless 3D Human Motion Capture from Images. In: Li, S.Z., Jain, A.K. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7488-4_38
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DOI: https://doi.org/10.1007/978-1-4899-7488-4_38
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