Advertisement

Region-Based vs. Edge-Based Registration for 3D Motion Capture by Real Time Monoscopic Vision

  • David Antonio Gómez Jáuregui
  • Patrick Horain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5496)

Abstract

3D human motion capture by real-time monocular vision without using markers can be achieved by registering a 3D articulated model on a video. Registration consists in iteratively optimizing the match between primitives extracted from the model and the images with respect to the model position and joint angles. We extend a previous color-based registration algorithm with a more precise edge-based registration step. We present an experimental analysis of the residual error vs. the computation time and we discuss the balance between both approaches.

Keywords

3D motion capture monocular vision 3D / 2D registration region matching edges matching 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agarwal, A., Triggs, B.: Recovering 3D human pose from monocular image. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 44–58 (2006)CrossRefGoogle Scholar
  2. 2.
    Borgefors, G.: Distance transformations in digital images. Computer Vision, Graphics and Image processing 34, 344–371 (1986)CrossRefGoogle Scholar
  3. 3.
    Cheung, G., Baker, S., Kanade, T.: Shape-from-silhouette for articulated objects and its use for human body kinematics estimation and motion capture. In: Computer vision and pattern recognition, Madison, Wisconsin, USA, pp. 16–22 (2003)Google Scholar
  4. 4.
    Deriche, R.: Fast algorithms for low-level vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 78–87 (1990)CrossRefGoogle Scholar
  5. 5.
    Franco, J.-S., Boyer, E.: Une approche hybride pour calculer l’enveloppe visuelle d’objets complexes. In: ORASIS 2003, pp. 67–74. Gérardmer (2003)Google Scholar
  6. 6.
    Fontmarty, M., Lerasle, F., Danes, P.: Data Fusion within a modified Annealed Particle Filter dedicated to Human Motion Capture. In: IEEE / RSJ International Conference on Intelligent Robots and Systems IROS 2007, San Diego, CA, USA, October 29-November 2, pp. 3391–3396 (2007)Google Scholar
  7. 7.
    Gómez Jáuregui, D.A., Horain, P., Baroud, F.: Acquisition 3D des gestes par vision monoscopique en temps réel. In: Conférence MajecSTIC 2008, Marseille, France (2008)Google Scholar
  8. 8.
    Horain, P., Bomb, M.: 3D Model Based Gesture Acquisition Using a Single Camera. In: Proceedings of IEEE Workshop on Applications of Computer Vision WACV 2002, Orlando, Florida, December 3-4, pp. 158–162 (2002)Google Scholar
  9. 9.
    Lu, S., Huang, G., Samaras, D., Metaxas, D.: Model-based integration of visual cues for hand tracking. In: Proceedings of IEEE workshop on Motion and Video Computing, Orlando, Florida, pp. 119–124 (2002)Google Scholar
  10. 10.
    Marques Soares, J., Horain, P., Bideau, A., Nguyen, M.H.: Acquisition 3D du geste par vision monoscopique en temps réel et téléprésence. In: Actes de l’atelier Acquisition du geste humain par vision artificielle et applications, pp. 23–27. Toulouse (2004)Google Scholar
  11. 11.
    Mori, G., Malik, J.: Recovering 3D human body configurations using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 28, 1052–1062 (2006)CrossRefGoogle Scholar
  12. 12.
    Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 208–313 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Pang, J., Qing, L., Huang, Q., Jiang, S.: Monocular Tracking 3D People by Gaussian Process Spatio-Temporal Variable Model. In: International Conference on Image Processing, ICIP 2007, San Antonio, Texas, USA, vol. 5, pp. 41–44 (2007)Google Scholar
  14. 14.
    Poppe, R.W.: Vision-based human motion analysis: An Overview. Computer Vision and Image Understanding 108(1-2), 4–18 (2007)CrossRefGoogle Scholar
  15. 15.
    Sminchisescu, C., Triggs, B.: Estimating Articualted Human Motion with Covariance Scaled Sampling. International Journal of Robotics Research 22, 371–393 (2003)CrossRefGoogle Scholar
  16. 16.
    Urtasun, R., Fleet, D.J., Fua, P.: 3D people tracking with gaussian process dynamical models. In: Proceedings of the Conference on Computer Vision and Pattern Recognition CVPR 2006, New York, vol. 1, pp. 238–245 (2006)Google Scholar
  17. 17.
    Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. IEEE Computer Vision and Pattern Recognition 1, 511 (2001)Google Scholar
  18. 18.
    Wright Jr., R.S., Lipchak, B., Haemel, N.: OpenGL SuperBible: Comprehensive Tutorial and Reference, 4th edn., pp. 127–172. Addison-Wesley Professional, Ann Arbor (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • David Antonio Gómez Jáuregui
    • 1
    • 2
  • Patrick Horain
    • 1
    • 2
  1. 1.INRIA / Projet MIRAGESLe Chesnay CedexFrance
  2. 2.Institut TELECOM ; TELECOM & Management SudParisEvry CedexFrance

Personalised recommendations