Skip to main content

3D Model-Based Tracking of the Human Body in Monocular Gray-Level Images

  • Conference paper
Computer Vision/Computer Graphics Collaboration Techniques (MIRAGE 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4418))

Abstract

This paper presents a model-based approach to monocular tracking of human body using a non-calibrated camera. The tracking in monocular images is realized using a particle filter and an articulated 3D model with a cylinder-based representation of the body. In modeling the visual appearance of the person we employ appearance-adaptive models. The predominant orientation of the gradient combined with ridge cues provides strong orientation responses in the observation model of the particle filter. The phase that is measured using the Gabor filter contributes towards strong localization of the body limbs. The potential of our approach is demonstrated by tracking of the human body on real videos.

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. Kortenkamp, D., Huber, E., Bonasso, R.P.: Recognizing and interpreting gestures on a mobile robot. In: Proc. Nat. Conf. on Artificial Intelligence, pp. 915–921 (1996), citeseer.ist.psu.edu/kortenkamp96recognizing.html

  2. Cham, T., Rehg, J.: A multiple hypothesis approach to figure tracking. In: Int. Conf. on Computer Vision and Patt. Recognition, pp. 239–245 (1999)

    Google Scholar 

  3. Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. Int. J. Comput. Vision 61(2), 185–205 (2005)

    Article  Google Scholar 

  4. Fritsch, J., Schmidt, J., Kwolek, B.: Kernel particle filter for real-time 3d body tracking in monocular color images. In: IEEE Int. Conf. on Face and Gesture Rec., Southampton, UK, pp. 567–572. IEEE Computer Society Press, Los Alamitos (2006)

    Google Scholar 

  5. Wren, C., et al.: Pfinder: Real-time tracking of the human body. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 780–785 (1997)

    Article  Google Scholar 

  6. Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: IEEE Int. Conf. on Pattern Recognition, pp. 126–133. IEEE Computer Society Press, Los Alamitos (2000)

    Google Scholar 

  7. Sigal, L., et al.: Tracking loose-limbed people. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 421–428. IEEE Computer Society Press, Los Alamitos (2004)

    Google Scholar 

  8. Rosales, R., et al.: Estimating 3d body pose using uncalibrated cameras. In: Int. Conf. on Computer Vision and Pattern Recognition, pp. 821–827 (2001)

    Google Scholar 

  9. Kehl, R., Bray, M., Gool, L.V.: Markerless full body tracking by integrating multiple cues. In: ICCV Workshop on Modeling People and Human Interaction, Beijing, China (2005)

    Google Scholar 

  10. Sidenbladh, H., Black, M., Fleet, D.: Stochastic tracking of 3d human figures using 2d image motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  11. Sminchisescu, C., Triggs, B.: Mapping minima and transitions of visual models. In: European Conference on Computer Vision, Copenhagen (2002)

    Google Scholar 

  12. Urtasum, R., Fleet, D.J., Fua, P.: Monocular 3-d tracking of the golf swing. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 932–938. IEEE Computer Society Press, Los Alamitos (2005)

    Google Scholar 

  13. Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. Int. J. of Computer Vision 29, 5–28 (1998)

    Article  Google Scholar 

  14. Jones, J., Palemer, L.: An evaluation of the two dimensional gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology 58, 1233–1258 (1987)

    Google Scholar 

  15. Nixon, M., Aguado, A.: Feature extraction and image processing. Newnes, Oxford (2002)

    Google Scholar 

  16. Fleet, D.: Disparity from local weighted phase-correlation. In: Proc. IEEE Int. Conf. on System Man and Cybernetics (SMC), pp. 46–48. IEEE Computer Society Press, Los Alamitos (1994)

    Google Scholar 

  17. Jepson, A.D., Fleet, D.J., El-Maraghi, T.: Robust on-line appearance models for visual tracking. PAMI 25(10), 1296–1311 (2003)

    Google Scholar 

  18. Nestares, O., et al.: Efficient spatial-domain implementation of a multiscale image representation based on gabor functions. J. Electronic Imaging 7, 166–173 (1998)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

André Gagalowicz Wilfried Philips

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Kwolek, B. (2007). 3D Model-Based Tracking of the Human Body in Monocular Gray-Level Images. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2007. Lecture Notes in Computer Science, vol 4418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71457-6_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71457-6_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71456-9

  • Online ISBN: 978-3-540-71457-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics