access icon free Improved number plate localisation algorithm and its efficient field programmable gate arrays implementation

Number plate localisation is a very important stage in an automatic number plate recognition (ANPR) system and is computationally intensive. This study presents a low complexity with high-detection rate number plate localisation algorithm based on morphological operations together with an efficient multiplier-less architecture based on that algorithm. The proposed architecture has been successfully implemented and tested using a Mentor Graphics RC240 FPGA (field programmable gate arrays) development board equipped with a 4M-gate Xilinx Virtex-4 LX40. Two database sets sourced from the UK and Greece and including 1000 and 307 images, respectively, both with a resolution of 640 × 480, have been used for testing. Results achieved have shown that the proposed system can process an image in 4.7 ms, while achieving a 97.8% detection rate and consuming only 33% of the available area of the FPGA.

Inspec keywords: image processing; vehicles; image resolution; field programmable gate arrays

Other keywords: field programmable gate array; 4M-gate Xilinx Virtex-4 LX40; multiplierless architecture; efficient field programmable gate arrays implementation; plate localisation algorithm; Mentor Graphics RC240 FPGA; high-detection rate number plate localisation algorithm

Subjects: Optical, image and video signal processing; Image recognition; Logic and switching circuits; Transportation; Computer vision and image processing techniques; Logic circuits

References

    1. 1)
      • 11. Wang, Y., Lin, W., Horng, S.: ‘A sliding window technique for efficient license plate localization based on discrete wavelet transform’, Expert Syst. Appl., 2011, 38, pp. 31423146 (doi: 10.1016/j.eswa.2010.08.106).
    2. 2)
      • 16. Kanamori, T., Amano, H., Arai, M., Konno, D., Nanba, T., Ajioka, Y.: ‘Implementation and evaluation of a high speed license plate recognition system on an FPGA’. Proc. Seventh IEEE Int. Conf. Computer and Information Technology, 2007, pp. 567572.
    3. 3)
      • 2. Arth, C., Leistner, C., Bischof, H.: ‘TRIcam: an embedded platform for remote traffic surveillance’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006, pp. 125125.
    4. 4)
      • 12. Anagnostopoulos, C., Alexandropoulos, T., Loumos, V., Kayafas, E.: ‘Intelligent traffic management through MPEG-7 vehicle flow surveillance’. Proc. IEEE Int. Symp. Modern Computing, 2006, vol. 9, pp. 377391.
    5. 5)
      • 7. Chang, S., Chen, L., Chung, Y., Chen, S.: ‘Automatic license plate recognition’, IEEE Trans. Intell. Transp. Syst., 2004, 5, pp. 4253 (doi: 10.1109/TITS.2004.825086).
    6. 6)
      • 8. Wang, F., Man, L., Wang, B., Xiao, Y., Pan, W., Lu, X.: ‘Fuzzy-based algorithm for color recognition of license plates’, J. Pattern Recognit. Lett., 2008, 29, (7), pp. 10071020 (doi: 10.1016/j.patrec.2008.01.026).
    7. 7)
      • 21. Xpower Tutorial: ‘FPGA design’ (Xilinx, July 2002).
    8. 8)
      • 9. Kim, K.I., Jung, K., Park, S., Kim, H.: ‘Support vector machines for texture classification’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, pp. 15421550 (doi: 10.1109/TPAMI.2002.1046177).
    9. 9)
      • 17. Shih, F., Wu, Y.: ‘Decomposition of arbitrary gray-scale morphological structuring elements’, Pattern Recognit., 2005, 38, pp. 23232332 (doi: 10.1016/j.patcog.2005.04.003).
    10. 10)
      • 19. ‘RC240 datasheet’ (Mentor Graphics Corporation, January. 2010).
    11. 11)
      • 15. Bellas, N., Chai, S.M., Dwyer, M., Linzmeiser, D.: ‘FPGA implementation of a license plate recognition SoC using automatically generated streaming accelerators’. Proc. 20th Int. Conf. Parallel and Distributed Processing Symposium, April 2006, pp. 8.
    12. 12)
      • 14. Cancer, H., Gecin, H.S., Alkar, A.Z.: ‘Efficient embedded neural-network based license plate recognition system’, IEEE Trans. Veh. Technol., 2008, 57, pp. 26752683 (doi: 10.1109/TVT.2008.915524).
    13. 13)
      • 20. ‘PixelStreams user manual’ (Mentor Graphics Corporation, January 2010).
    14. 14)
      • 1. Juan, J., Xu, J.: ‘Research of overall program on highway toll collection system’. Proc. Int. Conf. Information Science and Technology, March 2011, pp. 12181221.
    15. 15)
      • 13. Clemens, A., Florian, L., Horst, B.: ‘Real-time license plate recognition on an embedded DSP-platform’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007, pp. 18.
    16. 16)
      • 3. Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Psoroulas, I.D., Loumos, V., Kayafas, E.: ‘License plate recognition from still images and video sequences: a survey’, IEEE Trans. Intell. Transp. Syst., 2008, 9, (3), pp. 377391 (doi: 10.1109/TITS.2008.922938).
    17. 17)
      • 10. Kim, K.I., Jung, K., Kim, J.H.: ‘Color texture-based object detection: an application to license plate localization’. Patten Recognition with Support Vector Machines, 2002, (LNCS, 2388/2002), pp. 321335.
    18. 18)
      • 4. CitySync Limited, http://www.citysync.co.uk/, accessed January 2012.
    19. 19)
      • 6. Bai, H., Liu, C.: ‘A hybrid license plate extraction method based on edge statistics and morphology’. Proc. 17th Int. Conf. Pattern Recognition, 2004, vol. 2, pp. 831834.
    20. 20)
      • 5. Vargas, M., Toral, S.L., Barrero, F., Cortés, F.: ‘A license plate extraction algorithm based on edge statistics and region growing’. Lecture Notes in Computer Science, 2009, vol. 5716/2009, pp. 317326.
    21. 21)
      • 18. ‘PAL user manual’ (Mentor Graphics Corporation, January 2010).
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