Skip to main content

Using Saliency-Based Visual Attention Methods for Achieving Illumination Invariance in Robot Soccer

  • Conference paper
  • 1871 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7500)

Abstract

In order to be able to beat the world champion human soccer team in the year 2050, soccer playing robots will need to have very robust vision systems that can cope with drastic changes in illumination conditions. However, the current vision systems are still brittle and they require exhaustive and repeated color calibration procedures to perform acceptably well. In this paper, we investigate the suitability of biologically inspired saliency-based visual attention models for developing robust vision systems for soccer playing robots while focusing on the illumination invariance aspect of the solution. The experiment results demonstrate successful and accurate detection of the ball even when the illumination conditions change continuously and dramatically.

Keywords

  • Visual Attention
  • Generalize Regression Neural Network
  • Color Constancy
  • Color Segmentation
  • Robot Platform

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. Frintrop, S., Rome, E., Christensen, H.I.: Computational Visual Attention Systems and Their Cognitive Foundations: A Survey. ACM Transactions on Applied Perception 7(1), 1–39 (2010)

    CrossRef  Google Scholar 

  2. Sridharan, M., Stone, P.: Color learning and illumination invariance on mobile robots: A survey. Robotics and Autonomous Systems 57(6-7), 629–644 (2009)

    CrossRef  Google Scholar 

  3. Forsyth, D.A.: A novel algorithm for color constancy. International Journal of Computer Vision 5(1), 5–35 (1990)

    CrossRef  Google Scholar 

  4. Klinker, G.J., Shafer, S.A., Kanade, T.: A physical approach to color image understanding. International Journal of Computer Vision 4, 7–38 (1990)

    CrossRef  Google Scholar 

  5. Finlayson, G.D., Hordley, S.D., Hubel, P.M.: Color by correlation: A simple, unifying framework for color constancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1209–1221 (2001)

    CrossRef  Google Scholar 

  6. Brainard, D.H., Freeman, W.T.: Bayesian color constancy. Journal of the Optical Society of America A, Optics, Image Science, and Vision 14(7), 1393–1411 (1997)

    CrossRef  Google Scholar 

  7. Schulz, D., Fox, D.: Bayesian color estimation for adaptive vision-based robot localization. In: IROS (2004)

    Google Scholar 

  8. Luan, X., Qi, W., Song, D., Chen, M., Zhu, T., Wang, L.: Illumination invariant color model for object recognition in robot soccer. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010, Part II. LNCS, vol. 6146, pp. 680–687. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  9. Rasolzadeh, B., Björkmann, M., Huebner, K., Kragic, D.: An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World. The International Journal of Robotics Research 29(2-3), 133–154 (2009)

    CrossRef  Google Scholar 

  10. Frintrop, S.: VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search. LNCS (LNAI), vol. 3899. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  11. Frintrop, S., Nüchter, A., Pervölz, K., Surmann, H., Mitri, S., Hertzberg, J.: Attentive Classification. International Journal of Applied Artificial Intelligence in Engineering Systems 1(1) (2009)

    Google Scholar 

  12. Garcia, J.F., Rodríguez, F.J., Matellán, V., Fernández, C.: Saliency map based attention control for the RoboCup SPL. In: Workshop of Physical Agents (2010)

    Google Scholar 

  13. Itti, L., Koch, C., Niebur, E.: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)

    CrossRef  Google Scholar 

  14. Tsotsos, J.K., Culhane, S.M., Kei Wai, W.Y., Lai, Y., Davis, N., Nuflo, F.: Modeling visual attention via selective tuning. Artificial Intelligence 78(1-2), 507–545 (1995)

    CrossRef  Google Scholar 

  15. Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 4(4), 219–227 (1985)

    Google Scholar 

  16. Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 136(12), 97–136 (1980)

    CrossRef  Google Scholar 

  17. Itti, L.: Models of Bottom-Up and Top-Down Visual Attention. PhD thesis, California Institute of Technology (2000)

    Google Scholar 

  18. Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40(10-12), 1489–1506 (2000)

    CrossRef  Google Scholar 

  19. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (2002)

    CrossRef  Google Scholar 

  20. Li, Z., Fang, T., Huo, H., Zhu, J.: Color conspicuity map based on wavelet low-pass pyramid for popping out contours of salient objects. Optical Engineering 49(5), 050502 (2010)

    Google Scholar 

  21. Engel, S., Zhang, X., Wandell, B.: Colour tuning in human visual cortex measured with functional magnetic resonance imaging. Nature 388(6637), 68–71 (1997)

    CrossRef  Google Scholar 

  22. Specht, D.F.: A general regression neural network. IEEE Transactions on Neural Networks 2(6), 568–576 (1991)

    CrossRef  Google Scholar 

  23. Itti, L., Rees, G., Tsotsos, J.K.: Models of bottom-up attention and saliency. Neurobiology of Attention 582, 1–11 (1980)

    Google Scholar 

  24. Gouaillier, D., Hugel, V., Blazevic, P., Kilner, C., Monceaux, J., Lafourcade, P., Marnier, B., Serre, J., Maisonnier, B.: Mechatronic design of NAO humanoid. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 2124–2129. IEEE Press, Piscataway (2009)

    Google Scholar 

  25. The RoboCup Standard Platform League, http://www.tzi.de/spl

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Daniş, F.S., Meriçli, T., Akın, H.L. (2013). Using Saliency-Based Visual Attention Methods for Achieving Illumination Invariance in Robot Soccer. In: Chen, X., Stone, P., Sucar, L.E., van der Zant, T. (eds) RoboCup 2012: Robot Soccer World Cup XVI. RoboCup 2012. Lecture Notes in Computer Science(), vol 7500. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39250-4_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39250-4_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39249-8

  • Online ISBN: 978-3-642-39250-4

  • eBook Packages: Computer ScienceComputer Science (R0)