Abstract
Reconfigurability and parallel computing capability of field programmable gate array (FPGA) devices are highly exploited in real-time digital image and video processing applications. In this field, real-time traffic road signs detection systems present a huge interest since they help to assist drivers and decrease accidents. In this paper, we propose an FPGA-based hardware implementation of road signs detection and identification system. The proposed system can achieve real-time video constraints while assuring a high-level accuracy in terms of detection rate. The performance of the system in terms of processing latency was evaluated relatively to the reaction distance, the braking distance and the vehicle speed. The evaluation results show that our system can support real-time driving conditions until the speed of 110 km/h. To prove the validity of the proposed implementation, a hardware co-simulation strategy was applied. This is based on the use of Matlab/Xilinx system generator. A comparison of the co-simulation results shows the effectiveness of the developed architecture.
Similar content being viewed by others
References
Dembele, W.F., Kuhn, P.P.E., Ganley, R.T.: FPGA implementation of driver assistance camera algorithms. Technical report (2010)
Souani, C., Faiedh, H., Besbes, K.: Efficient algorithm for automatic road sign recognition and its hardware implementation. J. Real Time Image Proc. 9(1), 79–93 (2014)
Hechri, A., Hmida, R., Mtibaa, A.: Robust road lanes and traffic signs recognition for driver assistance system. Int. J. Comput. Sci. Eng. 10(1–2), 202–209 (2015)
Murphy-Chutorian, E., Trivedi, M.M.: N-tree disjoint-set forests for maximally stable extremal regions. In: BMVC, pp. 739–748, September 2006
Par, K., Tosum, O.: Real-time traffic sign recognition with map fusion on multicore/many-core architectures. J. Appl. Sci. 9(2), 231–250 (2012). (Acta Polytechnica Hungarica)
De La Escalera, A., Moreno, L.E., Salichs, M.A., Armingol, J.M.: Road traffic sign detection and classification. IEEE Trans. Industr. Electron. 44(6), 848–859 (1997)
Le, T.T., Tran, S.T., Mita, S., Nguyen, T.D.: Real time traffic sign detection using color and shape-based features. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds.) Intelligent Information and Database Systems, pp. 268–278. Springer, Berlin (2010)
Loy, G., Barnes, N.: Fast shape-based road sign detection for a driver assistance system. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004. (IROS 2004) Proceedings, vol. 1, pp. 70–75 (2004)
Zadeh, M.M., Kasvand, T., Suen, C.Y.: Localization and recognition of traffic signs for automated vehicle control systems. In: Intelligent Systems and Advanced Manufacturing, pp. 272–282. International Society for Optics and Photonics (1998)
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)
Escalera, S., Baró, X., Pujol, O., Vitrià, J., Radeva, P.: Background on traffic sign detection and recognition. In: Traffic-Sign Recognition Systems, pp. 5–13. Springer, London (2011)
Glavtchev, V., Muyan-Ozcelik, P., Ota, J.M., Owens, J.D.: Feature-based speed limit sign detection using a graphics processing unit. In: IEEE Intelligent Vehicles Symposium (IV), pp. 195–200, June 2011
Rudorfer, M.: Design and implementation of a classification algorithm for speed limit traffic sign recognition. Thesis report, Department of Machine Tools and Factory Management Division of Industrial Automation, June 2014
Eichner, M.L., Breckon, T.P.: Integrated speed limit detection and recognition from real-time video. In: Proceeding on IEEE Intelligent Vehicle Symposium, The Netherlands, 2008
Han, Y., Oruklu, E.: Real-time traffic sign recognition based on Zynq FPGA and arm SOCS. International IEEE Conference on Electro/Information Technology, pp. 373–376, 2014
Muyan-Ozcelik, P. Glavtchev, V. Ota, J.M, Owens, J.D.: A template-based approach for real-time speed-limit sign recognition on an embedded system using GPU computing. In: Proceeding on 32nd DAGM Conference on Pattern Recognition, pp. 162–171, 2010
Ugolotti, R., Nashed Youssef, S.G., Cagnoni, S.: Real-time GPU based road sign detection and classification. Chapter Parallel Probl. Solving Nat. 1, 153–162 (2012)
Dyczkowski, K. Gadecki, P., Kulakowski, A.: Traffic sign recognition system. In: World Conference on Soft Computing, San Francisco, USA, May 23–26, 2011
Fang, C.Y., Chen, S.W., Fuh, C.S.: Road-sign detection and tracking. IEEE Trans. Veh. Technol. 52(5), 1329–1341 (2003)
Hechri, A., Mtibaa, A.: Robust road sign recognition system for autonomous mobile robot. Int. J. Comput. Sci. Eng. Syst. 6(1), 19–29 (2012)
Prieto, M., Allen, A.: Using self-organizing maps in the detection and recognition of road signs. Image Vis. Comput. 27, 673–683 (2009)
Waite, S., Oruklu, E.: FPGA-based traffic sign recognition for advanced driver assistance systems. J. Transp. Technol. 3(1), 1–16 (2013)
Brkic, K.: An overview of traffic sign detection methods. Department of Electronics, Microelectronics, Computer and Intelligent Systems Faculty of Electrical Engineering and Computing, vol. 3, p. 10000, Unska (2010)
Zakir, U.A. Leonce, N.J., Edirisinghe, E.A.: Road sign segmentation based on color spaces: a comparative study. In: Proceeding on the 11th IASTED International Conference on Computer Graphics and Imaging (CGIM), Innsbruck, Austria (2010)
Lai, C.: An efficient real-time traffic sign recognition system for intelligent vehicles with smart phones. In: Proceeding in International Conference on Technologies and Applications of Artificial Intelligence, Hsinchu, pp. 195–202 (2010)
Soendoro, D., Supriana, I.: Traffic sign recognition with color-based method shape-arc estimation and SVM. In: proceedings of International Conference on Electrical Engineering and Informatics, Bandung, pp. 1–6 (2011)
Yabuki, N., Matsuda, Y., Fukui, Y., Miki, S.: Region detection using color similarity. In: Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 98–101 (1999)
Schiekel, C.: A fast traffic sign recognition algorithm for gray value images. In: Computer Analysis of Images and Patterns. 8th International Conference, CAIP’99 Ljubljana, Slovenia, September 1–3, 1999 Proceedings. Springer, Berlin, Heidelberg (1999)
Ach, R. Luth, N., Techmer, A.: Real-time detection of traffic signs on a multi-core processor. In: Proceeding of the IEEE Intelligent Vehicles Symposium, pp. 307–312 (2008)
De la Escalera, A., Armingol, J., Pastor, J., Rodriguez, F.: Visual sign information extraction and identification by deformable models for intelligent vehicles. IEEE Trans. Intell. Transp. Syst. 5(2), 57–68 (2004)
The MathWorks User’s Guide. http://www.opal-rt.com
Barnes, N., Zelinsky, A.: Real-time radial symmetry for speed sign detection. In: Proceeding on IEEE Intelligent Vehicles Symposium, pp. 566–571, 2004
Antolovic, D.: Review of the Hough transform method, with an implementation of the fast Hough variant for line detection. Department of Computer Science, Indiana University, Indiana (2008)
Souki, M.A., Boussaid, L., Abid, M.: An embedded system for real-time traffic sign recognizing. In: 3rd International Design and Test Workshop, 2008. IDT 2008, pp. 273–276 (2008)
Adam, A., Ioannidis, C.: Automatic road-sign detection and classification based on support vector machines and hog descriptors. Int Soc Photogramm. Remote Sens. ISPRS Ann Photogramm. Remote Sens. Spatial Inform. Sci. II-5, 1–7 (2014)
Wali, S.B., Hannan, M.A., Abdullah, S., Hussain, A., Samad, S.A.: Shape matching and color segmentation based traffic sign detection system. Threshold 90, 255 (2015)
Cao, T.P., Deng, G., Elton, D.: Grayscale image segmentation for real-time traffic sign recognition: the hardware point of view. In: Proceeding of SPIE Electronic Imaging, pp. 724405–724405. International Society for Optics and Photonics, February 2009
Kiran, C.G., Prabhu, L.V., Rahiman, V.A., Rajeev, K.: Support vector machine learning based traffic sign detection and shape classification using distance to borders and distance from center features. In: TENCON IEEE Region 10 Conference, pp. 1–6. IEEE, November 2008
Martín, P., Bueno, E., Rodríguez, F.J., Machado, O., Vuksanovic, B.: An FPGA-based approach to the automatic generation of VHDL code for industrial control systems applications: a case study of MSOGIs implementation. Math. Comput. Simul. 91, 178–192 (2013)
Van Beeck, K., Heylen, F., Meel, J., Goedemé, T.: Comparative study of model-based hardware design tools. In: Proceedings of European Conference on the Use of Modern Electronics in ICT, ECUMICT, vol. 5, p. 2860, February 2010
Suthar, A.C., Vayada, M., Patel, C.B., Kulkarni, G.R.: Hardware software co-simulation for image processing applications. Int. J. Comput. Sci. Issues 9(2), 560–562 (2012)
Wang, C.C., Shi, C., Brodersen, R.W., Marković, D.: An automated fixed-point optimization tool in MATLAB XSG/SynDSP environment. ISRN Signal Process. 2011, 17 (2011). doi:10.5402/2011/414293
Moctezuma, J.C., Sanchez, S., Alvarez, R., Sánchez, A.: Architecture for filtering images using Xilinx system generator. In: Proceedings of the 2nd WSEAS International Conference on Computer Engineering and Applications, pp. 284–289. World Scientific and Engineering Academy and Society (WSEAS), January 2008
Karthigaikumar, P., Kirubavathy, K.J., Baskaran, K.: FPGA based audio watermarking—Covert communication. Microelectron. J. 42(5), 778–784 (2011)
Toledo, A., Vicente-Chicote, C., Suardíaz, J., Cuenca, S.: Xilinx system generator based HW components for rapid prototyping of computer vision SW/HW systems. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) Pattern Recognition and Image Analysis, pp. 667–674. Springer, Berlin (2005)
Stereopolis database. (2015). http://www.itowns.fr/roadsign.php. Accessed 20 April 2015
German traffic sign recognition benchmark. http://benchmark.ini.rub.de. Accessed 20 April 2015
Miyata, S., Yanou, A., Nakamura, H., Takehara, S.: Road sign feature extraction and recognition using dynamic image processing. Int. J. Innov. Comput. Inform. Control 5(11), 4105–4113 (2009)
IRMAK, H.: Real time traffic sign recognition system on FPGA. Thesis, Graduate School of Natural and Applied Sciences of Middle East Technical University (2010)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hmida, R., Ben Abdelali, A. & Mtibaa, A. Hardware implementation and validation of a traffic road sign detection and identification system. J Real-Time Image Proc 15, 13–30 (2018). https://doi.org/10.1007/s11554-016-0579-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11554-016-0579-x