Using Genetic Algorithms for Real-Time Object Detection

  • J. Martínez-Gómez
  • J. A. Gámez
  • I. García-Varea
  • V. Matellán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5949)


This article presents a new approach to mobile robot vision based on genetic algorithms. The major contribution of this proposal is the real-time adaptation of genetic algorithms, which are generally used offline. In order to achieve this goal, the execution time must be as short as possible. The scope of this system is the Standard Platform category of the RoboCup soccer competition. The system developed detects and estimates distance and orientation to key elements on a football field, such as the ball and goals. Different experiments have been carried out within an official RoboCup environment.


  1. 1.
    Rofer, T., Brunn, R., Dahm, I., Hebbel, M., Hoffmann, J., Jungel, M., Laue, T., Lotzsch, M., Nistico, W., Spranger, M.: GermanTeam 2004. Team Report RoboCup (2004)Google Scholar
  2. 2.
    Wasik, Z., Saffiotti, A.: Robust color segmentation for the robocup domain. In: Pattern Recognition, Proc. of the Int. Conf. on Pattern Recognition (ICPR), vol. 2, pp. 651–654 (2002)Google Scholar
  3. 3.
    Jüngel, M., Hoffmann, J., Lötzsch, M.: A real-time auto-adjusting vision system for robotic soccer. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 214–225. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Coath, G., Musumeci, P.: Adaptive arc fitting for ball detection in robocup. In: APRS Workshop on Digital Image Analysing, pp. 63–68 (2003)Google Scholar
  5. 5.
    Mitchell, M.: An Introduction to Genetic Algorithms (1996)Google Scholar
  6. 6.
    Whitley, L.: Cellular Genetic Algorithms. In: Proceedings of the 5th International Conference on Genetic Algorithms table of contents. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  7. 7.
    Foley, J.D., van Dam, A., Feiner, S.K., Hughes, J.F.: Computer graphics: principles and practice. Addison-Wesley Longman Publishing Co., Inc., Amsterdam (1990)Google Scholar
  8. 8.
    Borenstein, J., Everestt, H., Feng, L.: Where am I? Sensors and Methods for Mobile Robot Positioning (1996)Google Scholar
  9. 9.
    Bach, J., Jungel, M.: Using pattern matching on a flexible, horizon-aligned grid for robotic vision. Concurrency, Specification and Programming-CSP 1(2002), 11–19 (2002)Google Scholar
  10. 10.
    Moscato, P.: Memetic algorithms: a short introduction. Mcgraw-Hill’S Advanced Topics In Computer Science Series, pp. 219–234 (1999)Google Scholar
  11. 11.
    Fox, D., Burgard, W., Thrun, S.: Active markov localization for mobile robots (1998)Google Scholar
  12. 12.
    Negenborn, R.: Robot localization and kalman filters (2003)Google Scholar
  13. 13.
    Martínez-Gómez, J., José, A., Gámez, I.G.V.: An improved markov-based localization approach by using image quality evaluation. In: Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1236–1241 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • J. Martínez-Gómez
    • 1
  • J. A. Gámez
    • 1
  • I. García-Varea
    • 1
  • V. Matellán
    • 2
  1. 1.Computing Systems DepartmentUniversity of Castilla-La ManchaSpain
  2. 2.Dept. of Mechanical and Computer EngineeringUniversity of LeónSpain

Personalised recommendations