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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de la Escalera, A., Armingol, J.M., Mata, M.: Traffic sign recognition and analysis for intelligent vehicles. Image Vis. Comput. 21, 247–258 (2003)
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
Koschan, A., Abidi, M.: Digital color image processing. Wiley Interscience Publication, Canada (2000)
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)
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)
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)
Fang, C.-Y., Chen, S.-W., Fuh, C.-S.: Road-sign detection and tracking. IEEE Trans. Veh. Technol. 52(5), 1329–1341 (2003)
Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, New York (1995)
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
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)
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)
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
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
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
Egmont-Petersen, M., de Ridderb, D., Handels, H.: Image processing with neural networks—a review. Pattern Recognit. 35(10), 2279–2301 (2002)
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
Ozden, M., Polat, E.: A color image segmentation approach for content-based image retrieval. Pattern Recognit. 40, 1318–1325 (2007)
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)
Paclik, P., Novovicova, J., Pudil, P., Somol, P.: Road sign classification using Laplace kernel classifier. Pattern Recogn. Lett. 21, 1165–1173 (2000)
Douville, P.: Real-time classification of traffic signs. Real Time Imaging 6, 185–193 (2000)
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
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
Gonzalez, R.C., Woods, R.E.: Digital image processing, 2nd edn. Prentice Hall, Upper saddle river (2002)
Lukac, R., Plataniotis, K.N.: Color image processing: methods and applications. CRC Press/Taylor & Francis, Boca Raton (2007)
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)
Hsu, S.-H., Huang, C.-L.: Road sign detection and recognition using matching pursuit method. Image Vis. Comput. 19, 119–129 (2001)
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)
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
Acharya, T., Ray, A.K.: Image processing principles and applications. Wiley Interscience, New Jersey (2005)
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
Wen, W., Chen, X., Yang, J.: Detection of text on road signs from video. IEEE Trans. Intell. Transp. Syst. 6(4), 378–390 (2005)
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)
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)
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
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
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
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)
Mussi, L., et al.: GPU implementation of a road sign detector based on particle swarm optimization. J. Evol. Intell. 3, 155–169 (2010)
Laganiere, R.: Opencv 2 computer vision application programming cookbook. Packt Publishing, Birmingham (2011)
Opel insignia http://com.opel-microsites.com/insignia/
Volkswagen phaeton http://en.volkswagen.com/en/models/phaeton
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
Zaklouta, F., Stanciulescu, B.: Real-time traffic sign recognition in three stages. Robot. Auton. Syst. (2012). doi:10.1016/j.robot.2012.07.019
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)
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)
Chen, C.-L., Tai, C.-L.: Adaptive fuzzy color segmentation with neural network for road detections. Eng. Appl. Artif. Intell. 23, 400–410 (2010)
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
Mobileye http://www.mobileye.com/technology/applications/traffic-sign-detection/
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
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)
Irmak, H.: Real-time traffic sign recognition system on FPGA. Thesis, Middle East Technical University, Ankara, September 2010
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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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DOI: https://doi.org/10.1007/s11554-013-0348-z