Learning Visual Obstacle Detection Using Color Histogram Features

  • Saskia Metzler
  • Matthias Nieuwenhuisen
  • Sven Behnke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7416)

Abstract

Perception of the environment is crucial in terms of successfully playing soccer. Especially the detection of other players improves game play skills, such as obstacle avoidance and path planning. Such information can help refine reactive behavioral strategies, and is conducive to team play capabilities. Robot detection in the RoboCup Standard Platform League is particularly challenging as the Nao robots are limited in computing resources and their appearance is predominantly white in color like the field lines.

This paper describes a vision-based multilevel approach which is integrated into the B-Human Software Framework and evaluated in terms of speed and accuracy. On the basis of color segmented images, a feed-forward neural network is trained to discriminate between robots and non-robots. The presented algorithm initially extracts image regions which potentially depict robots and prepares them for classification. Preparation comprises calculation of color histograms as well as linear interpolation in order to obtain network inputs of a specific size. After classification by the neural network, a position hypothesis is generated.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aldebaran Robotics: Nao Robot Reference Manual, Version 1.10.10, Internal Report (2010)Google Scholar
  2. 2.
    Bradski, G.R.: The OpenCV Library (2000), http://opencv.willowgarage.com/
  3. 3.
    Daniş, F.S., Meriçli, T., Meriçli, Ç., Akın, H.L.: Robot Detection with a Cascade of Boosted Classifiers Based on Haar-Like Features. In: Ruiz-del-Solar, J., Chown, E., Ploeger, P.G. (eds.) RoboCup 2010. LNCS (LNAI), vol. 6556, pp. 409–417. Springer, Heidelberg (2010)Google Scholar
  4. 4.
    Fabisch, A., Laue, T., Röfer, T.: Robot Recognition and Modeling in the RoboCup Standard Platform League. In: Proc. 5th Workshop on Humanoid Soccer Robots at Humanoids (2010)Google Scholar
  5. 5.
    Fasola, J., Veloso, M.M.: Real-time Object Detection using Segmented and Grayscale Images. In: IEEE International Conference on Robotics and Automation, pp. 4088–4093 (2006)Google Scholar
  6. 6.
    Lange, S., Riedmiller, M.: Appearance-Based Robot Discrimination Using Eigenimages. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006. LNCS (LNAI), vol. 4434, pp. 499–506. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Lee, Y.: Handwritten Digit Recognition Using K Nearest-Neighbor, Radial-Basis Function, and Backpropagation Neural Networks. Neural Computation 3, 440–449 (1991)CrossRefGoogle Scholar
  8. 8.
    Liu, C.-L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten Digit Recognition: Benchmarking of State-of-the-art Techniques. Pattern Recognition 36(10), 2271–2285 (2003)MATHCrossRefGoogle Scholar
  9. 9.
    Mayer, G., Kaufmann, U., Kraetzschmar, G.K., Palm, G.: Neural Robot Detection in RoboCup. In: Wermter, S., Palm, G., Elshaw, M. (eds.) Biomimetic Neural Learning for Intelligent Robots. LNCS (LNAI), vol. 3575, pp. 349–361. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Röfer, T., Laue, T., Müller, J., Bösche, O., Burchardt, A., Damrose, E., Gillmann, K., Graf, C., de Haas, T.J., Härtl, A., Rieskamp, A., Schreck, A., Sieverdingbeck, I., Worch, J.H.: B-Human Team Report and Code Release 2009 (2009)Google Scholar
  11. 11.
    Ruiz-del-Solar, J., Verschae, R., Arenas, M., Loncomilla, P.: Play ball! fast and accurate multiclass visual detection of robots and its application to behavior recognition. IEEE Robotics Automation Magazine 17(4), 43–53 (2010)CrossRefGoogle Scholar
  12. 12.
    Sony Corporation: AIBO (1999), http://support.sony-europe.com/aibo/
  13. 13.
    Viola, P.A., Jones, M.J.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: CVPR, vol. 1, pp. 511–518 (2001)Google Scholar
  14. 14.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. Tech. Rep. 95-041, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (1995)Google Scholar
  15. 15.
    Wilking, D., Röfer, T.: Realtime Object Recognition Using Decision Tree Learning. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 556–563. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Saskia Metzler
    • 1
  • Matthias Nieuwenhuisen
    • 1
  • Sven Behnke
    • 1
  1. 1.Autonomous Intelligent Systems Group, Institute for Computer Science VIUniversity of BonnGermany

Personalised recommendations