Techniques for Designing an FPGA-Based Intelligent Camera for Robots

  • Miguel Contreras
  • Donald G. Bailey
  • Gourab Sen Gupta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 345)


This paper outlines useful techniques to design and develop an intelligent camera on a Field Programmable Gate Array (FPGA). Some of the development and testing issues of porting a software algorithm onto a hardware platform are discussed, as well as ways to avoid the corresponding problems. To demonstrate the importance of these techniques, an intelligent camera designed to calculate the position, orientation and identification of soccer playing robots is used as a case study.


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  1. 1.
    Bailey, D.G., Gupta, G.S., Contreras, M.: Intelligent Camera for Object Identification and Tracking. In: Kim, J.-H., Matson, E., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications. AISC, vol. 208, pp. 1003–1013. Springer, Heidelberg (2013)Google Scholar
  2. 2.
    Johnston, C., Gribbon, K., Bailey, D.: Implementing image processing algorithms on FPGAs. In: Eleventh Electronics New Zealand Conference (ENZCon 2004), Palmerston North, New Zealand, pp. 118–123 (2004)Google Scholar
  3. 3.
    Dias, F., Berry, F., Serot, J., Marmoiton, F.: Hardware, Design and Implementation Issues on a FPGA-Based Smart Camera. In: First ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC 2007), Vienna, Austria, pp. 20–26 (2007)Google Scholar
  4. 4.
    Lim, Y., Kleeman, L., Drummond, T.: Algorithmic Methodologies for FPGA-Based Vision. Machine Vision and Applications 24, 1197–1211 (2013)CrossRefGoogle Scholar
  5. 5.
    Contreras, M., Bailey, D.G., Gupta, G.S.: FPGA Implementation of Global Vision for Robot Soccer as a Smart Camera. In: Kim, J.-H., Matson, E., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications 2. AISC, vol. 274, pp. 657–666. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  6. 6.
    Davies, E.: Machine Vision: Theory, Algorithms, Practicalities, 3rd edn., pp. 193–194. Morgan Kauffmann, San Francisco (2005)Google Scholar
  7. 7.
    Kulpa, Z.: Area and Perimeter Measurement of Blobs in Discrete Binary Pictures. Computer Graphics and Image Processing 6, 434–451 (1977)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Ellis, T., Proffitt, D., Rosen, D., Rutkowski, W.: Measurement of the Lengths of Digitized Curved Lines. Computer Graphics and Image Processing 10, 333–347 (1979)CrossRefGoogle Scholar
  9. 9.
    Ramanath, R., Snyder, W., Bilbro, G., Sander, W.: Demosaicking methods for Bayer color arrays. Journal of Electronic Imaging 11, 306–315 (2002)CrossRefGoogle Scholar
  10. 10.
    Jean, R.: Demosaicing with The Bayer Pattern. Department of Computer Science, University of North Carolina (2010)Google Scholar
  11. 11.
    Malvar, H., Li-Wei, H., Cutler, R.: High-Quality Linear Interpolation for Demosaicing of Bayer-Patterned Color Images. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004), Montreal, Canada, pp. 485–488 (2004)Google Scholar
  12. 12.
    Gribbon, K., Bailey, D., Johnston, C.: Design Patterns for Image Processing Algorithm Development on FPGAs. In: IEEE TENCON of Region 10, Melbourne, Australia, pp. 1–6 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Miguel Contreras
    • 1
  • Donald G. Bailey
    • 1
  • Gourab Sen Gupta
    • 1
  1. 1.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand

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