Shape Based Round Object Detection Using Edge Orientation Histogram

  • Hamid Mobalegh
  • Lovísa Irpa Helgadóttir
  • Raúl Rojas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8371)


In this paper we introduce a shape based method to globally detect the ball in a RoboCup soccer scenario. The method can be used for any round object with detectable edges. The concept of integral images presented in Viola & Jones 2001, is used, however the integration is applied to a vector representation of the gradient orientation histogram of each pixel. The method takes advantage from the fact that large areas of the image can be filtered out, as these are only covered by straight edges. An overlapped binary search quickly reduces the search area and locates ball candidates in the image. The candidates are finally selected using an outlier elimination technique.


Gradient Vector Integral Image Edge Orientation Soccer Robot Orientation Histogram 
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.


  1. 1.
    Bonarini, A., Furlan, A., Malago, L., Marzorati, D., Matteucci, M., Migliore, D., Restelli, M., Sorrenti, D.: Milan Robocup Team 2009. In: RoboCup 2009 Graz, Graz, Austria (2009)Google Scholar
  2. 2.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8(6), 679–698 (1986)CrossRefGoogle Scholar
  3. 3.
    Coath, G., Musumeci, P.: Adaptive arc fitting for ball detection in robocup. In: Proceedings of APRS Workshop on Digital Image Analysing, Brisbane, Australia, pp. 63–68 (2003)Google Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
  5. 5.
    Hanek, R., Beetz, M.: The contracting curve density algorithm: Fitting parametric curve models to images using local self-adapting separation criteria. International Journal of Computer Vision 59, 233–258 (2004)CrossRefGoogle Scholar
  6. 6.
    Li, X., Lu, H., Xiong, D., Zhang, H., Zheng, Z.: A Survey on Visual Perception for RoboCup MSL Soccer Robots. International Journal of Advanced Robotic Systems 1 (2013)Google Scholar
  7. 7.
    Martins, D., Neves, A., Pinho, A.: Real-time generic ball recognition in RoboCup domain. In: Proc. of the Ibero- …, 11th edn. (2008)Google Scholar
  8. 8.
    Mitri, S., Frintrop, S., Pervolz, K., Surmann, H., Nuchter, A.: Robust object detection at regions of interest with an application in ball recognition. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, pp. 125–130 (April 2005)Google Scholar
  9. 9.
    Neves, A.J., Pinho, A.J., Martins, D.A., Cunha, B.: An efficient omnidirectional vision system for soccer robots: From calibration to object detection. Mechatronics 21(2), 399–410 (2011)CrossRefGoogle Scholar
  10. 10.
    Roberts, L.G.: Machine perception of three-dimensional solids. Technical report, DTIC Document (1963)Google Scholar
  11. 11.
    Treptow, A., Zell, A.: Real-time object tracking for soccer-robots without color information. Robotics and Autonomous Systems 48(1), 41–48 (2004)CrossRefGoogle Scholar
  12. 12.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, CVPR 2001, vol. 1 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Hamid Mobalegh
    • 1
  • Lovísa Irpa Helgadóttir
    • 1
  • Raúl Rojas
    • 1
  1. 1.Institut für InformatikFreie Universität BerlinGermany

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