Skip to main content

Markerless 3D Human Motion Capture from Images

  • Reference work entry
  • First Online:
Encyclopedia of Biometrics
  • 134 Accesses

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...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 899.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. E. Muybridge, Animals Locomotion (University of Pennsylvania, Philadelphia, 1887)

    Google Scholar 

  2. E.J. Marey, Le Mouvement (Editions Jaqueline Chambon, 1994). Réédition de 1894 des éditions Masson

    Google Scholar 

  3. 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)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. 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

    Google Scholar 

  6. 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)

    Google Scholar 

  7. E. Seemann, B. Leibe, B. Schiele, Multi-aspect detection of articulated objects, in Conference on Computer Vision and Pattern Recognition, New York, 2006

    Google Scholar 

  8. D. Ramanan, A. Forsyth, A. Zisserman, Tracking people by learning their appearance. IEEE Trans. Pattern Anal. Mach. Intell. 29, 65–81 (2007)

    Google Scholar 

  9. 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

    Google Scholar 

  10. H. Murase, R. Sakai, Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recognit. Lett. 17, 155–162 (1996)

    Google Scholar 

  11. R. Urtasun, D. Fleet, A. Hertzman, P. Fua, Priors for people tracking from small training sets, in International Conference on Computer Vision, Beijing, 2005

    Google Scholar 

  12. D. Ormoneit, H. Sidenbladh, M. Black, T. Hastie, Learning and tracking cyclic human motion, in Neural Information Processing Systems, Vancouver, 2001, pp. 894–900

    Google Scholar 

  13. 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

    Google Scholar 

  14. N. Troje, Decomposing biological motion: a framework for analysis and synthesis of human gait patterns. J. Vis. 2, 371–387 (2002)

    Google Scholar 

  15. Q. He, C. Debrunner, Individual recognition from periodic activity using Hidden Markov Models, in IEEE Workshop on Human Motion, Austin, 2000

    Google Scholar 

  16. S. Niyogi, E.H. Adelson, Analyzing and recognizing walking figures in XYT, in Conference on Computer Vision and Pattern Recognition, Seattle, 1994

    Google Scholar 

  17. J. Little, J. Boyd, Recognizing people by their gait: the shape of motion. Videre 1, 1–32 (1986)

    Google Scholar 

  18. 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

    Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this entry

Cite this entry

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

Download citation

Publish with us

Policies and ethics