Advertisement

Robust and Efficient Object Recognition for a Humanoid Soccer Robot

  • Alexander Härtl
  • Ubbo Visser
  • Thomas Röfer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8371)

Abstract

Static color classification as a first processing step of an object recognition system is still the de facto standard in the RoboCup Standard Platform League (SPL). Despite its efficiency, this approach lacks robustness with regard to changing illumination. We propose a new object recognition system where objects are found based on color similarities. Our experiments with line, goal, and ball recognition show that the new system is real-time capable on a contemporary NAO (version 3.2 and above). We show that the detection rate is comparable to color-table-based object recognition under static lighting conditions and substantially better under changing illumination.

Keywords

Object Recognition Center Circle Goal Post Line Detection Hough Transformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bruce, J., Balch, T., Veloso, M.: Fast and Inexpensive Color Image Segmentation for Interactive Robots. In: 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2061–2066 (2000)Google Scholar
  2. 2.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (6), 679–698 (1986)Google Scholar
  3. 3.
    Chernov, N., Lesort, C.: Least Squares Fitting of Circles. Journal of Mathematical Imaging and Vision 23(3), 239–252 (2005)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Duda, R.O., Hart, P.E.: Use of the Hough Transformation To Detect Lines and Curves in Pictures. Communications of the ACM 15(1), 11–15 (1972)CrossRefGoogle Scholar
  5. 5.
    Gevers, T., Smeulders, A.W.M.: Color-based object recognition. Pattern Recognition 32(3), 453–464 (1999)CrossRefGoogle Scholar
  6. 6.
    Härtl, A.: Robuste, echtzeitfähige Bildverarbeitung für einen humanoiden Fußballroboter. Master’s thesis, Universität Bremen (2012)Google Scholar
  7. 7.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004)Google Scholar
  8. 8.
    Hojjatoleslami, S.A., Kittler, J.: Region Growing: A New Approach. IEEE Transactions on Image Processing 7(7), 1079–1084 (1998)CrossRefGoogle Scholar
  9. 9.
    Jähne, B.: Digital Image Processing, 6th edn. Springer (2005)Google Scholar
  10. 10.
    Jain, R.C., Kasturi, R., Schunck, B.G.: Machine vision. McGraw-Hill (1995)Google Scholar
  11. 11.
    Jamzad, M., Sadjad, B.S., Mirrokni, V.S., Kazemi, M., Chitsaz, H., Heydarnoori, A., Hajiaghai, M.T., Chiniforooshan, E.: A Fast Vision System for Middle Size Robots in RoboCup. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, pp. 71–80. Springer, Heidelberg (2002)Google Scholar
  12. 12.
    Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62–66 (1979)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Press, W., Teukolsky, S., Flannery, B., Vetterling, W.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press (1992)Google Scholar
  14. 14.
    Reinhardt, T.: Kalibrierungsfreie Bildverarbeitungsalgorithmen zur echtzeitfähigen Objekterkennung im Roboterfußball. Master’s thesis, Hochschule für Technik, Wirtschaft und Kultur Leipzig (2011)Google Scholar
  15. 15.
    Röfer, T.: Region-Based Segmentation with Ambiguous Color Classes and 2-D Motion Compensation. In: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds.) RoboCup 2007. LNCS (LNAI), vol. 5001, pp. 369–376. Springer, Heidelberg (2008)Google Scholar
  16. 16.
    Röfer, T., Laue, T., Müller, J., Fabisch, A., Feldpausch, F., Gillmann, K., Graf, C., de Haas, T.J., Härtl, A., Humann, A., Honsel, D., Kastner, P., Kastner, T., Könemann, C., Markowsky, B., Riemann, O.J.L., Wenk, F.: B-Human Team Report and Code Release 2011 (2011), http://www.b-human.de/downloads/bhuman11_coderelease.pdf
  17. 17.
    Scharstein, D., Szeliski, R.: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision 47(1-3), 7–42 (2002)CrossRefzbMATHGoogle Scholar
  18. 18.
    Swain, M.J., Ballard, D.H.: Color Indexing. International Journal of Computer Vision 7(1), 11–32 (1991)CrossRefGoogle Scholar
  19. 19.
    Volioti, S., Lagoudakis, M.G.: Histogram-Based Visual Object Recognition for the 2007 Four-Legged RoboCup League. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 313–326. Springer, Heidelberg (2008)Google Scholar
  20. 20.
    Zhang, C., Wang, P.: A New Method of Color Image Segmentation Based on Intensity and Hue Clustering. In: 15th International Conference on Pattern Recognition, vol. 3, pp. 613–616 (2000)Google Scholar
  21. 21.
    Zucker, S.W.: Region Growing: Childhood and Adolescence. Computer Graphics and Image Processing 5(3), 382–399 (1976)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Alexander Härtl
    • 1
  • Ubbo Visser
    • 1
  • Thomas Röfer
    • 2
  1. 1.Department of Computer ScienceUniversity of MiamiCoral GablesUSA
  2. 2.Cyber-Physical SystemsDeutsches Forschungszentrum für Künstliche IntelligenzBremenGermany

Personalised recommendations