Very High Speed, Close Field, Object Positioning Using Tri-linear CCDs

  • David Jahshan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)


To be able to effectively intercept and control a soccer ball travelling at high speed, it is useful to be able to accurately track the position of the ball as it approaches the robot. In this paper we present a method that can calculate the position in two dimensions at thousands of frames per second using a pair of inexpensive tri-linear CCDs. Each CCD gathers RGB information, which is then colour segmented. This data is then fused to calculate the location of the object in 2D. Further, the amount of processing required to detect these objects is low, and can be accomplished using inexpensive electronic components.


Clock Cycle Object Position Observation Area Colour Segmentation Soccer Ball 
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.
    Demigny, D., Kessal, L., Bourgiba, R., Boudouani, N.: How to use high speed recongifgurable fpga for real time image processing. IEEE, Los Alamitos (2000)Google Scholar
  2. 2.
    Andren-Dinerf, J., et al.: Chipvision a vision system for robots based on reconfigurable hardware. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Benkrid, K., Crookes, D., Smith, J., Benkrid, A.: High level programming for real time fpga based video processing. IEEE, Los Alamitos (2000)Google Scholar
  4. 4.
    Moore, K., Jaffe, J., Benjamin, B.: Development of a new underwater bathymetric laser imaging system. Journal of Atmospheric and Oceanic Technology 17(8) (2000)Google Scholar
  5. 5.
    Lucchese, L., Mitra, S.K.: Color image segmentation: A state-of-the-art survey. In: Proc. of the Indian National Science Academy (2001)Google Scholar
  6. 6.
    Nakagawa, M., Shibasaki, R., Kagawa, Y.: Fusing stereo linear ccd images and laser range data for building 3D urban model. In: Geospatial Theory, Processing and Applications. ISPRS, vol. XXXIV, part 4 (2002)Google Scholar
  7. 7.
    NEC: upd3729 5000 pixels x 3 color ccd linear image sensor datasheet (2001)Google Scholar
  8. 8.
    Curry, P.M., Morgan, F., Kilmartin, L.: Xilink fpga implementation of an image classifier for object detection applications. IEEE, Los Alamitos (2001)Google Scholar
  9. 9.
    Sony. Ilx558k 5340-pixel x 3-line ccd linear colour sensor datasheetGoogle Scholar
  10. 10.
    Kagawa, Y.: Automatic acquisition of 3d city data with air-borne tls and laser scanner. Graduate School of Frontier Sciences, Institute of Environmental Studies, University of Tokyo, Japan (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David Jahshan
    • 1
  1. 1.Department of Electrical and Electronic EngineeringThe University Of MelbourneAustralia

Personalised recommendations