Fast Object Detection with Foveated Imaging and Virtual Saccades on Resource Limited Robots

  • Adrian Ratter
  • David Claridge
  • Jayen Ashar
  • Bernhard Hengst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)


This paper describes the use of foveated imaging and virtual saccades to identify visual objects using both colour and edge features. Vision processing is a resource hungry operation at the best of times. When the demands require robust, real time performance with a limited embedded processor, the challenge is significant. Our domain of application is the RoboCup Standard Platform League soccer competition using the Aldebaran Nao robot. We describe algorithms that use a combination of down-sampled colour images and high resolution edge detection to identify objects in varying lighting conditions. Optimised to run in real time on autonomous robots, these techniques can potentially be applied in other resource limited domains.


Edge Detection Goal Post Ball Detection Saliency Image Foveated Image 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Board of trustees: Robocup,
  2. 2.
    Ashar, J., Claridge, D., Hall, B., Hengst, B., Nguyen, H., Pagnucco, M., Ratter, A., Robinson, S., Sammut, C., Vance, B., White, B., Zhu, Y.: RoboCup Standard Platform League - rUNSWift 2010. In: Australasian Conference on Robotics and Automation (2010)Google Scholar
  3. 3.
    Camacho, P., Arrebola, F., Sandoval, F.: Multiresolution Sensors With Adaptive Structure. In: Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society, IECON 1998 (1998)Google Scholar
  4. 4.
    Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm For Model Fitting With Applications to Image Analysis and Automated Cartography. Commun. ACM 24, 381–395 (1981), MathSciNetCrossRefGoogle Scholar
  5. 5.
    North, A.: Object Recognition From Sub-Sampled Image Processing. Honours thesis, The University of New South Wales (2005)Google Scholar
  6. 6.
    Pham, K.C.: Incremental Learning of Vision Recognition Using Ripple Down Rules. Honours thesis, The University of New South Wales (2005)Google Scholar
  7. 7.
    Ratter, A., Hengst, B., Hall, B., White, B., Vance, B., Claridge, D., Nguyen, H., Ashar, J., Robinson, S., Zhu, Y.: rUNSWift Team Report 2010 Robocup Standard Platform League. Tech. rep., School of Computer Science and Engineering, University of New South Wales (2010)Google Scholar
  8. 8.
    Röfer, T., Jüngel, M.: Fast and Robust Edge-Based Localization in the Sony Four-Legged Robot League. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 262–273. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Röfer, T., Laue, T., Müller, J., Bösche, O., Burchardt, A., Damrose, E., Gillmann, K., Graf, C., ry de Haas, T.J., Härtl, A., Rieskamp, A., Schreck, A., Sieverdingbeck, I., Worch, J.H.: B-Human Team Report and Code Release (2009),
  10. 10.
    von Hundelshausen, F., Rojas, R.: Tracking Regions. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 250–261. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Adrian Ratter
    • 1
  • David Claridge
    • 1
  • Jayen Ashar
    • 1
  • Bernhard Hengst
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of New South Wales, UNSWSydneyAustralia

Personalised recommendations