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.
Chapter PDF
Similar content being viewed by others
Keywords
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
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)
Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (6), 679–698 (1986)
Chernov, N., Lesort, C.: Least Squares Fitting of Circles. Journal of Mathematical Imaging and Vision 23(3), 239–252 (2005)
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)
Gevers, T., Smeulders, A.W.M.: Color-based object recognition. Pattern Recognition 32(3), 453–464 (1999)
Härtl, A.: Robuste, echtzeitfähige Bildverarbeitung für einen humanoiden Fußballroboter. Master’s thesis, Universität Bremen (2012)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004)
Hojjatoleslami, S.A., Kittler, J.: Region Growing: A New Approach. IEEE Transactions on Image Processing 7(7), 1079–1084 (1998)
Jähne, B.: Digital Image Processing, 6th edn. Springer (2005)
Jain, R.C., Kasturi, R., Schunck, B.G.: Machine vision. McGraw-Hill (1995)
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)
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62–66 (1979)
Press, W., Teukolsky, S., Flannery, B., Vetterling, W.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press (1992)
Reinhardt, T.: Kalibrierungsfreie Bildverarbeitungsalgorithmen zur echtzeitfähigen Objekterkennung im Roboterfußball. Master’s thesis, Hochschule für Technik, Wirtschaft und Kultur Leipzig (2011)
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)
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
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)
Swain, M.J., Ballard, D.H.: Color Indexing. International Journal of Computer Vision 7(1), 11–32 (1991)
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)
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)
Zucker, S.W.: Region Growing: Childhood and Adolescence. Computer Graphics and Image Processing 5(3), 382–399 (1976)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Härtl, A., Visser, U., Röfer, T. (2014). Robust and Efficient Object Recognition for a Humanoid Soccer Robot. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds) RoboCup 2013: Robot World Cup XVII. RoboCup 2013. Lecture Notes in Computer Science(), vol 8371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44468-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-662-44468-9_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44467-2
Online ISBN: 978-3-662-44468-9
eBook Packages: Computer ScienceComputer Science (R0)