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Efficient algorithm for automatic road sign recognition and its hardware implementation

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Abstract

The automatic detection of road signs is an application that alerts the vehicle’s driver of the presence of signals and invites him to react on time in the aim to avoid potential traffic accidents. This application can thus improve the road safety of persons and vehicles traveling in the road. Several techniques and algorithms allowing automatic detection of road signs are developed and implemented in software and do not allow embedded application. We propose in this work an efficient algorithm and its hardware implementation in an embedded system running in real time. In this paper we propose to implement the application of automatic recognition of road signs in real time by optimizing the techniques used in different phases of the recognition process. The system is implemented in a Virtex4 FPGA family which is connected to a camera mounted in the moving vehicle. The system can be integrated into the dashboard of the vehicle. The performance of the system shows a good compromise between speed and efficiency.

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References

  1. de la Escalera, A., Armingol, J.M., Mata, M.: Traffic sign recognition and analysis for intelligent vehicles. Image Vis. Comput. 21, 247–258 (2003)

    Article  Google Scholar 

  2. Broggi, A., Cerri, P., Medici, P., Porta, P.P., Ghisio, G.: Real time road signs recognition. In: IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, June 13–15, 2007, pp. 981–986

  3. Koschan, A., Abidi, M.: Digital color image processing. Wiley Interscience Publication, Canada (2000)

    Google Scholar 

  4. de la Escalera, A., Armingol, J.M., Pastor, J.M., Rodríguez, F.J.: Visual sign information extraction and identification by deformable models for intelligent vehicles. IEEE Trans. Intell. Transp. Syst. 5(2), 57–68 (2004)

    Article  Google Scholar 

  5. de la Escalera, A., Moreno, L.E., Salichs, M.A., Armingol, J.M.: Road traffic sign detection and classification. IEEE Trans. Ind. Electron 44(6), 848–859 (1997)

    Article  Google Scholar 

  6. Fanga, C.Y., Fuhb, C.S., Yena, P.S., Cherngc, S., Chen, S.W.: An automatic road sign recognition system based on a computational model of human recognition processing. Comput. Vis. Image Underst. 96, 237–268 (2004)

    Article  Google Scholar 

  7. Fang, C.-Y., Chen, S.-W., Fuh, C.-S.: Road-sign detection and tracking. IEEE Trans. Veh. Technol. 52(5), 1329–1341 (2003)

    Article  Google Scholar 

  8. Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, New York (1995)

    Google Scholar 

  9. Huang, C.-L., Hsu, S.-H.: Road sign interpretation using matching pursuit method. In: 4th IEEE Southwest Symposium on Image Analysis and Interpretation, 2–4 April 2000, Austin, USA, pp. 202–206

  10. Pérez, E., Javidi, B.: Nonlinear distortion-tolerant filters for detection of road signs in background noise. IEEE Trans. Veh. Technol. 51(3), 567–576 (2002)

    Article  Google Scholar 

  11. Samet, H., Tamminen, M.: Efficient component labeling of images of arbitrary dimension represented by linear bintrees. IEEE Trans. Pattern Anal. Mach. Intell. 10(4), 579–586 (1988)

    Article  Google Scholar 

  12. Ohara, H., Nishikawa, I., Miki, S., Yabuki, N.: Detection and recognition of road signs using simple layered neural networks. In: Proceedings of the 9th International Conference on Neural Information Processing (ICONIP’02), Singapore, vol. 2, 18–22 Nov. 2002, pp. 626–630

  13. Fleyeh, H.: Color detection and segmentation for road and signs. In: Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 1–3 December 2004, pp. 809–814

  14. Torresen, J., Bakke, J.W., Sekanina, L.: Efficient recognition of speed limit signs. In: 2004 IEEE Intelligent Transportation Systems Conference, Washington, D.C., USA, October 3–6, 2004, pp. 652–656

  15. Egmont-Petersen, M., de Ridderb, D., Handels, H.: Image processing with neural networks—a review. Pattern Recognit. 35(10), 2279–2301 (2002)

    Article  MATH  Google Scholar 

  16. Riaz, M., Kang, G., Kim, Y., Pan, S., Park, J.: Efficient image retrieval using adaptive segmentation of HSV color space. In: International Conference on Computational Sciences and Its Applications ICCSA 2008, Perugia, Italy, June 30–July 3, 2008, pp. 491–496

  17. Ozden, M., Polat, E.: A color image segmentation approach for content-based image retrieval. Pattern Recognit. 40, 1318–1325 (2007)

    Article  MATH  Google Scholar 

  18. Barnes, N., Zelinsky, A., Fletcher, L.S.: Real-time speed sign detection using the radial symmetry detector. IEEE Trans. Intell. Transp. Syst. 9(2), 322–332 (2008)

    Article  Google Scholar 

  19. Paclik, P., Novovicova, J., Pudil, P., Somol, P.: Road sign classification using Laplace kernel classifier. Pattern Recogn. Lett. 21, 1165–1173 (2000)

    Article  MATH  Google Scholar 

  20. Douville, P.: Real-time classification of traffic signs. Real Time Imaging 6, 185–193 (2000)

    Article  Google Scholar 

  21. Dahyot, R., Charbonnier, P., Heitz, F.: Robust visual recognition of colour images. In: IEEE International Conference of Computer and Vision Pattern Recognition, CVPR 2000, Hilton Head Island, USA, vol. 1, 13–15 June 2000, pp. 685–690

  22. Vicen-Bueno, R., Gil-Pita, R., Jarabo-Amores, M.P. L’opez-Ferreras, F.: Complexity reduction in neural networks applied to traffic sign recognition tasks. In: 13th European Signal Processing Conference EUSIPCO 2005, Antalya, Turkey, September 4–8, 2005

  23. Gonzalez, R.C., Woods, R.E.: Digital image processing, 2nd edn. Prentice Hall, Upper saddle river (2002)

    Google Scholar 

  24. Lukac, R., Plataniotis, K.N.: Color image processing: methods and applications. CRC Press/Taylor & Francis, Boca Raton (2007)

    Google Scholar 

  25. Escalera, S., Radeva, P.: Fast greyscale road sign model matching and recognition. In: Vitria, J., et al. (eds.) Recent Advances in Artificial Intelligence Research and Development, pp. 69–76. IOS Press, Amsterdam (2004)

    Google Scholar 

  26. Hsu, S.-H., Huang, C.-L.: Road sign detection and recognition using matching pursuit method. Image Vis. Comput. 19, 119–129 (2001)

    Article  Google Scholar 

  27. Maldonado-Bascón, S., Lafuente-Arroyo, S., Gil-Jiménez, P., Gómez-Moreno, H., López-Ferreras, F.: Road-sign detection and recognition based on support vector machines. IEEE Trans. Intell. Transp. Syst. 8(2), 264–278 (2007)

    Article  Google Scholar 

  28. Zin, T.T., Hama, H.: Robust road sign recognition using standard deviation. In: 2004 IEEE Intelligent Transportation Systems Conference, Washington, D.C., USA, October 3–4 2004, pp. 429–434

  29. Acharya, T., Ray, A.K.: Image processing principles and applications. Wiley Interscience, New Jersey (2005)

    Book  Google Scholar 

  30. Vitabile, S., Gentile, A., Sorbello, F.: A neural network based automatic road signs recognizer. In: International Joint Conference on Neural Networks, IJCNN ‘02, 12–17 May 2002, Honolulu, HI, USA, vol. 3, pp. 2315–2320

  31. Wen, W., Chen, X., Yang, J.: Detection of text on road signs from video. IEEE Trans. Intell. Transp. Syst. 6(4), 378–390 (2005)

    Article  Google Scholar 

  32. Gao, X.W., Podladchikova, L., Shaposhnikov, D., Hong, K., Shevtsova, N.: Recognition of traffic signs based on their colour and shape features extracted using human vision models. J. Vis. Commun. Image Represent. 17(4), 675–685 (2006)

    Article  Google Scholar 

  33. Gao, X.W., Podladchikova, L., Shaposhnikov, D., Hong, K., Shevtsova, N.: Recognition of traffic signs based on their colour and shape features extracted using human vision models. J. Vis. Commun. Image Represent. 17, 675–685 (2006)

    Article  Google Scholar 

  34. Nguwi, Y.-Y., Kouzani, A.Z: Automatic road sign recognition using neural networks. In: International Joint Conference on Neural Networks, Vancouver, Canada, July 16–21, 2006, pp. 3955–3962

  35. Lauzière, Y.B., Gingras, D., Ferrie, F.P.: A model-based road sign identification system. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8–14 December 2001, Kauai, Hawaii, vol. 1, pp. 1163–1171

  36. Shuang-dong, Z., Yi, Z., Xiao-feng, L.: Detection for triangle traffic sign based on neural network. In: IEEE International Conference on Vehicular Electronics and Safety, 14–16 October 2005, Xi’an, China, pp. 25–28

  37. Inland transport committee, Convention on road signs and signals. Economic commission for Europe, Vienna, 8 November 1968 (E/CONF.56/17/Rev.1/Amend.1)

  38. Mussi, L., et al.: GPU implementation of a road sign detector based on particle swarm optimization. J. Evol. Intell. 3, 155–169 (2010)

    Article  Google Scholar 

  39. Laganiere, R.: Opencv 2 computer vision application programming cookbook. Packt Publishing, Birmingham (2011)

    Google Scholar 

  40. Opel insignia http://com.opel-microsites.com/insignia/

  41. Volkswagen phaeton http://en.volkswagen.com/en/models/phaeton

  42. Park, J. et al.: A 92mW real-time traffic sign recognition system with robust light and dark adaptation. IEEE Asian Solid-State Circuits Conference, Jeju, Korea, November 14–16, 2011, pp. 397–400

  43. Zaklouta, F., Stanciulescu, B.: Real-time traffic sign recognition in three stages. Robot. Auton. Syst. (2012). doi:10.1016/j.robot.2012.07.019

    Google Scholar 

  44. Prieto, M.S., Allen, A.R.: Using self-organising maps in the detection and recognition of road signs. Image Vis. Comput. 27, 673–683 (2009)

    Article  Google Scholar 

  45. Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Netw. 32, 323–332 (2012)

    Article  Google Scholar 

  46. Chen, C.-L., Tai, C.-L.: Adaptive fuzzy color segmentation with neural network for road detections. Eng. Appl. Artif. Intell. 23, 400–410 (2010)

    Article  Google Scholar 

  47. Muller, M., Braun, A., Nienhuser, D., Zollner, J.M.: Design of an automotive traffic sign recognition system targeting a multi-core SoC implementation. In: Design, Automation and Test in Europe Conference and Exhibition (DATE), 8–12 March 2010, Dresden, Germany, pp. 532–537

  48. Mobileye http://www.mobileye.com/technology/applications/traffic-sign-detection/

  49. Torresen, J., Bakke, J.W., Sekanina, L.: Recognizing speed limit sign numbers by Evolvable Hardware. In: 8th International Conference, Birmingham, UK, September 18–22, 2004, pp. 682–691

  50. Par, K., Tosun, O.: Real-time traffic sign recognition with map fusion on multicore/many-core architectures. Acta Polytechnica Hungarica J. Appl Sci. 9(2), 231–250 (2012)

  51. Irmak, H.: Real-time traffic sign recognition system on FPGA. Thesis, Middle East Technical University, Ankara, September 2010

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Authors and Affiliations

  1. Laboratory of Microelectronics and Instrumentation, Faculty of Sciences, Avenue de l’environnement, 5019, Monastir, Tunisia

    Chokri Souani, Hassene Faiedh & Kamel Besbes

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  1. Chokri Souani
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  2. Hassene Faiedh
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  3. Kamel Besbes
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Correspondence to Chokri Souani.

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Souani, C., Faiedh, H. & Besbes, K. Efficient algorithm for automatic road sign recognition and its hardware implementation. J Real-Time Image Proc 9, 79–93 (2014). https://doi.org/10.1007/s11554-013-0348-z

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