Skip to main content

Solutions for Model-Based Analysis of Human Gait

  • Conference paper
Pattern Recognition (DAGM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2781))

Included in the following conference series:

Abstract

The analysis-by-synthesis concept is applied in markerless human gait analysis. Human locomotion is approximated by means of adaptive tracking with a 3D model that moves in exactly the same manner as the subject in front of the cameras. This paper focuses on two particular problems: (1) the inverse mapping of pixels from the synthetic image back to the surface of the 3D model, and (2) the acquisition of initial values for automatic initialization of the 3D model for subsequent reliable tracking. Some interesting initialization constraints arise when the analysis-by-synthesis concept is applied in medical human gait analysis. The moving subject is segmented with an improved dual difference technique, which uses the gradient norms of real camera images. The most important assumption is that human gait is almost completely periodic. This allows a much more robust approach whereby the keyframe animation technique serves to synthesize artificial motion patterns using approximately correct joint angles.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Moeslund, T.B., Granum, E.: A Survey of Computer Vision-Based Human Motion Capture. In: Computer Vision and Image Understanding: CVIU (2001)

    Google Scholar 

  2. Lander, J.: Over My Dead, Polygonal Body, Game Developer Magazine, Source code Skeletal Deformation in OpenGL (October 1999), http://www.darwin3d.com/gdm1999.htm

  3. Winter, D.A.: Biomechanics and motor control of human movement. Wiley- Interscience, USA (1990)

    Google Scholar 

  4. Wachter, S.: Verfolgung von Personen in monokularen Bildfolgen, Vice Versa (1997)

    Google Scholar 

  5. Koch, R.: Dynamic 3D Scene Analysis through Synthesis Feedback Control. IEEE Trans. Patt. Anal. Mach. Intell., analysis and synthesis 15(6), 556–568 (1993)

    Article  Google Scholar 

  6. Foley, D.J., van Dam, A., Feiner, S.K., Hughes, J.F.: Computer Graphics: Principles and Practice. Addison-Wesley, Reading (1997)

    Google Scholar 

  7. Stevens, M.R., Beveridge, J.R.: Integrating Graphics and Vision for Object Recognition. Kluver Academic Publishers, Boston (2001)

    MATH  Google Scholar 

  8. Black, M.J.: The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields. Computer Vision and Image Understanding 63(1), 75–104 (1996)

    Article  MathSciNet  Google Scholar 

  9. Mecke, R.: Grauwertbasierte Bewegungsschatzung in monokularen Bildsequenzen unter besonderer Berucksichtigung bildspezifischer Storungen. Shaker Verlag, Aachen (1999)

    Google Scholar 

  10. Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape Context and Chamfer Matching in Cluttered Scenes. In: Proc. Conf. Computer Vision and Pattern Recognition, Madison, USA (June 2003)

    Google Scholar 

  11. Shreiner, D.: OpenGL(R) Reference Manual, see first http://www.opengl.org/

  12. VITRONIC Dr.-Ing. Stein Bildverarbeitungssysteme, http://www.vitus.de/

  13. Kameda, Y., Minoh, M.: A Human Motion Estimation Method using 3-successive video frames. In: Proceedings of International Conference on Virtual Systems and Multimedia 1996, pp. 135–140 (1996)

    Google Scholar 

  14. Luhmann, T.: Nahbereichsphotogrammetrie: Grundlagen, Methoden und Anwendungen. Wichmann Verlag (2000)

    Google Scholar 

  15. Collins, R. T., Gross, R., Shi, J.: Silhouette-based Human Identification from Body Shape and Gait. In: Conference on Face and Gesture (2002)

    Google Scholar 

  16. Frigo, M., Johnson, S.G.: FFTW manual online, http://www.fftw.org/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Calow, R., Michaelis, B., Al-Hamadi, A. (2003). Solutions for Model-Based Analysis of Human Gait. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_69

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-45243-0_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40861-1

  • Online ISBN: 978-3-540-45243-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics