Skip to main content
Log in

Aircraft identification integrated into an airport surface surveillance video system

  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract.

A video aircraft identification algorithm, based on tail number recognition, is proposed as part of a global airport surveillance video system. The recognition procedure searches and detects the presence of the tail number in the image and then recognizes the tail number using pattern matching techniques. The identification system has been designed to deal with airport real images, taking into account letter size differences and potential deformations. Finally, the aircraft identification system calculates the joint probability of each tail number in the airport database. The tail number maximizing the joint probability is selected. Results show that the identification procedure achieves a robust identification using the database.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. ASMGCS (2000) European Manual of Advanced Surface Movement and Control Systems, draft, vol 1: Operational requirements. Draft version 04. 08/24/2000

  2. Bäck T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, Oxford, UK

  3. Berlanga A, García J, Molina JM, Besada J, Portillo J (2002) OCR parameters Tuning by means evolution strategies to aircraft’s tail number recognition. In: Proceedings of the congress on evolutionary computation (CEC 2002), IEEE world congress on computational intelligence (IJCNN, FUZZ-IEEE, ICEC), Honolulu, HI, May 2002, pp 902-907

  4. Besada J, García J, Varona A, González G, Molina JM, Portillo J (2001) Image-based automatic surveillance for airport surface. In: Proceedings of the 4th international conference on information fusion (Fusion 2001), Montreal, August 2001, pp (WeA1) 11-18

  5. Bokser M (1992) Omnidocument technologies. Proc IEEE 80:1066-1078

    Article  Google Scholar 

  6. CAPTS (1998) Cooperative Area Precision Tracking System. Final report of test results. Frankfurt Airport, Airsys ATM Gmbh, January 1998

  7. Castleman KR (1996) Digital image processing. Prentice-Hall, Upper Saddle River, NJ

  8. ECAC (1994) Proceedings of the ECAC APATSI and EC Workshop on A-SMGCS, Frankfurt, Germany, April 1994

  9. García J, Molina JM, Besada J, Portillo J (2002) Robust object tracking with fuzzy shape estimation. In: Proceedings of the 5th international conference on information fusion (Fusion 2002), Annapolis, MD, 8-11 July 2002, pp 64-71

  10. Giardina CR, Dougherty ER (1988) Morphological methods in image and signal processing. Prentice-Hall, Upper Saddle River, NJ

  11. ICAO (1981) Convention on civil aviation

  12. ICAO (1986) Manual of SMGCS. ICAO, Document 9476-AN/927

  13. Jain R, Kasturi R, Schunck BG (1995) Machine vision. McGraw-Hill, New York

  14. Lipton AJ, Fujiyoshi H, Patil RS (1998) Moving target classification and tracking from real-time video. In: Proceedings of the IEEE workshop on image understanding, pp 129-136

  15. Molina JM, García J, Berlanga A, Besada J, Portillo J (2002) Automatic video system for aircraft identification. In: Proceedings of the 5th international conference on information fusion (Fusion 2002), Annapolis, MD, 8-11 July 2002, pp 1387-1394

  16. Mori S, Suen CY, Yamamoto K (1992) Historical review of OCR research and development. Proc IEEE 80(7):1029-1058

    Article  Google Scholar 

  17. PAMI (2000) Special section on video surveillance. IEEE Trans PAMI 22(8)

  18. Parker JR (1996) Algorithms for image processing and computer vision. Wiley, New York

  19. Portillo J, Besada J, García J, Molina JM, Varona A, González G (2001) MARIA: preliminary results of an AENA R&D project on airport surface surveillance based on CTV. In: Proceedings of the international conference on airport surveillance sensors, Paris, December 2001, pp 138-145

  20. Pratt WK (1991) Digital image processing. Wiley, New York

  21. Russ JC (1995) The image processing handbook. CRC Press, Boca Raton, FL

  22. Sanka M, Hlavac V, Boyle R (1999) Image processing, analysis and machine vision. Brooks/Cole, Pacific Grove, CA

  23. Schewefel HP (1988) Evolutionary learning optimum-seeking on parallel computer architectures. In: Proceedings of the international symposium on systems analysis and simulation: I. Theory and foundations, Berlin, September 1988. Akademie, Berlin, pp 217-225

  24. Schwabn CE, Rost DP (1985) Airport surface detection equipment. Proc IEEE (2)

  25. SYLETRACK (1997) Rapport d’evaluation du systéme SYLETRACK. ADP

  26. Törn A, Zilinskas A (1991) Global optimization. Lecture notes in computer science, vol 350. Springer, Berlin Heidelberg New York

  27. Trier ØD, Jain AK, Taxt T (1996) Feature extraction methods for character recognition - a survey. Patt Recog 29(4):641-662

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. A. Besada.

Additional information

Received: 7 November 2002, Accepted: 21 January 2004, Published online: 27 May 2004

Correspondence to: J.A. Besada

Rights and permissions

Reprints and permissions

About this article

Cite this article

Besada, J.A., Molina, J.M., García, J. et al. Aircraft identification integrated into an airport surface surveillance video system. Machine Vision and Applications 15, 164–171 (2004). https://doi.org/10.1007/s00138-004-0135-8

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-004-0135-8

Keywords:

Navigation