Speed limit sign detection and recognition system using SVM and MNIST datasets

  • Yassmina SaadnaEmail author
  • Ali Behloul
  • Saliha Mezzoudj
Original Article


This article presents a computer vision system for real-time detection and robust recognition of speed limit signs, specially designed for intelligent vehicles. First, a new segmentation method is proposed to segment the image, and the CHT transformation (circle hog transform) is used to detect circles. Then, a new method based on local binary patterns is proposed to filter segmented images in order to reduce false alarms. In the classification phase, a cascading architecture of two linear support vector machines is proposed. The first is trained on the GTSRB dataset to decide whether the detected region is a speed limit sign or not, and the second is trained on the MNIST dataset to recognize the sign numbers. The system achieves a classification recall of 99.81% with a precision of 99.08% on the GTSRB dataset; in addition, the system is also tested on the BTSD and STS datasets, and it achieves a classification recall of 99.39% and 98.82% with a precision of 99.05% and 98.78%, respectively, within a processing time of 11.22 ms.


Speed limit sign recognition Pattern recognition SVM Image segmentation LBP Vehicle safety 


Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest associated with this article.


  1. 1.
    Agudo D, Sánchez Á, Vélez JF, Moreno AB (2016) Real-time railway speed limit sign recognition from video sequences. In: 2016 international conference on systems, signals and image processing (IWSSIP). IEEE, pp 1–4Google Scholar
  2. 2.
    Mammeri A, Boukerche A, Feng J, Wang R (2013) North-American speed limit sign detection and recognition for smart cars. In: 2013 IEEE 38th conference on local computer networks workshops (LCN workshops). IEEE, pp 154–161Google Scholar
  3. 3.
    Kundu SK, Mackens P (2015) Speed limit sign recognition using MSER and artificial neural networks. In: 2015 IEEE 18th international conference on intelligent transportation systems (ITSC). IEEE, pp 1849–1854Google Scholar
  4. 4.
    Soetedjo A, Somawirata IK (2018) Speed limit traffic sign classification using multiple features matching. In: Kim K, Kim H, Baek N (eds) IT convergence and security 2017. Lecture notes in electrical engineering, vol 449. Springer, SingaporeCrossRefGoogle Scholar
  5. 5.
    Tsai CY, Liao HC, Feng YC (2016) A novel translation, rotation, and scale-invariant shape description method for real-time speed-limit sign recognition. In: International conference on advanced materials for science and engineering (ICAMSE). IEEE, pp 486–488Google Scholar
  6. 6.
    Gomes SL, Rebouças EDS, Neto EC, Papa JP, de Albuquerque VH, Rebouças Filho PP, Tavares JMR (2017) Embedded real-time speed limit sign recognition using image processing and machine learning techniques. Neural Comput Appl 28(1):573–584CrossRefGoogle Scholar
  7. 7.
    Liu B, Liu H, Luo X, Sun F (2012) Speed limit sign recognition using log-polar mapping and visual codebook. In International symposium on neural networks. Springer, Berlin, pp 247–256Google Scholar
  8. 8.
    Lim K, Lee T, Shin C, Chung S, Choi Y, Byun H (2014) Real-time illumination-invariant speed-limit sign recognition based on a modified census transform and support vector machines. In: Proceedings of the 8th international conference on ubiquitous information management and communication. ACM, p 92Google Scholar
  9. 9.
    Moutarde F, Bargeton A, Herbin A, Chanussot L (2007) Robust on-vehicle real-time visual detection of American and European speed limit signs, with a modular Traffic Signs Recognition system. In: Intelligent vehicles symposium, 2007 IEEE. IEEE, pp 1122–1126Google Scholar
  10. 10.
    Ishak KA, Sani MM, Tahir NM (2006) A speed limit sign recognition system using artificial neural network. In: 4th student conference on research and development, 2006. SCOReD 2006. IEEE, pp 127–131Google Scholar
  11. 11.
    Eichner ML, Breckon TP (2008) Integrated speed limit detection and recognition from real-time video. In: Intelligent vehicles symposium, 2008 IEEE. IEEE, pp 626–631Google Scholar
  12. 12.
    Miyata S (2017) Automatic recognition of speed limits on speed-limit signs by using machine learning. J Imaging 3(3):25CrossRefGoogle Scholar
  13. 13.
    Yan G, Yu M, Shi S, Feng C (2017) The recognition of traffic speed limit sign in hazy weather. J Intell Fuzzy Syst 33(2):873–883CrossRefGoogle Scholar
  14. 14.
    Peemen M, Mesman B, Corporaal H (2011) Speed sign detection and recognition by convolutional neural networks. In: Proceedings of the 8th international automotive congress, pp 162–170Google Scholar
  15. 15.
    Li Y, Mogelmose A, Trivedi MM (2016) Pushing the “Speed Limit”: high-accuracy US traffic sign recognition with convolutional neural networks. IEEE Trans Intell Veh 1(2):167–176CrossRefGoogle Scholar
  16. 16.
    Saadna Y, Behloul A (2017) An overview of traffic sign detection and classification methods. Int J Multimed Inf Retr 6(3):193–210CrossRefGoogle Scholar
  17. 17.
    Gómez-Moreno H, Maldonado-Bascón S, Gil-Jiménez P, Lafuente-Arroyo S (2010) Goal evaluation of segmentation algorithms for traffic sign recognition. IEEE Trans Intell Transp Syst 11(4):917–930CrossRefGoogle Scholar
  18. 18.
    Lim KH, Ang LM, Seng KP (2009) New hybrid technique for traffic sign recognition. In: International symposium on intelligent signal processing and communications systems, 2008. ISPACS 2008. IEEE, pp 1–4Google Scholar
  19. 19.
    Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259CrossRefGoogle Scholar
  20. 20.
    Stallkamp J, Schlipsing M, Salmen J, Igel C (2012) Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw 32:323–332CrossRefGoogle Scholar
  21. 21.
    Houben S, Stallkamp J, Salmen J, Schlipsing M, Igel C (2013) Detection of traffic signs in real-world images: the German Traffic Sign Detection Benchmark. In: The 2013 international joint conference on neural networks (IJCNN). IEEE, pp 1–8Google Scholar
  22. 22.
    Timofte R, Zimmermann K, Van Gool L (2014) Multi-view traffic sign detection, recognition, and 3D localisation. Mach Vis Appl 25(3):633–647CrossRefGoogle Scholar
  23. 23.
    Li S, Karatzoglou A, Gentile C (2016) Collaborative filtering bandits. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 539–548Google Scholar
  24. 24.
    Yakimov PY (2015) Preprocessing digital images for quickly and reliably detecting road signs. Pattern Recognit Image Anal 25(4):729–732CrossRefGoogle Scholar
  25. 25.
    Laguna R, Barrientos R, Blazquez LF, Miguel LJ (2014) Traffic sign recognition application based on image processing techniques. IFAC Proc Vol 47(3):104–109CrossRefGoogle Scholar
  26. 26.
    de Araujo AF, Constantinou CE, Tavares JMR (2014) New artificial life model for image enhancement. Expert Syst Appl 41(13):5892–5906CrossRefGoogle Scholar
  27. 27.
    de Araujo AF, Constantinou CE, Tavares JMR (2016) Smoothing of ultrasound images using a new selective average filter. Expert Syst Appl 60:96–106CrossRefGoogle Scholar
  28. 28.
    Gulo CASJ, de Arruda HF, de Araujo AF et al (2016) Efficient parallelization on GPU of an image smoothing method based on a variational model. J Real-Time Image Proc. Google Scholar
  29. 29.
    Gentile C, Li S, Kar P, Karatzoglou A, Etrue E, Zappella G (2016) On context-dependent clustering of bandits. arXiv preprint arXiv:1608.03544
  30. 30.
    Korda N, Szörényi B, Shuai L (2016) Distributed clustering of linear bandits in peer to peer networks. In: Journal of machine learning research workshop and conference proceedings, vol. 48. International Machine Learning Societ, pp 1301–1309Google Scholar
  31. 31.
    Li S (2016) The art of clustering bandits (Doctoral dissertation, Universita degli Studi dell’Insubria)Google Scholar
  32. 32.
    Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit 40(3):825–838CrossRefzbMATHGoogle Scholar
  33. 33.
    Ma Z, Tavares JMR, Jorge RN, Mascarenhas T (2010) A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Eng 13(2):235–246CrossRefGoogle Scholar
  34. 34.
    Oliveira RB, Mercedes Filho E, Ma Z, Papa JP, Pereira AS, Tavares JMR (2016) Computational methods for the image segmentation of pigmented skin lesions: a review. Comput Methods Programs Biomed 131:127–141CrossRefGoogle Scholar
  35. 35.
    Jodas DS, Pereira AS, Tavares JMR (2016) A review of computational methods applied for identification and quantification of atherosclerotic plaques in images. Expert Syst Appl 46:1–14CrossRefGoogle Scholar
  36. 36.
    Dey N, Ashour AS, Beagum S, Pistola DS, Gospodinov M, Gospodinova EP, Tavares JMR (2015) Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image denoising. J Imaging 1(1):60–84CrossRefGoogle Scholar
  37. 37.
    Aziz S, Youssef F (2018) Traffic sign recognition based on multi-feature fusion and ELM classifier. Procedia Comput Sci 127:146–153CrossRefGoogle Scholar
  38. 38.
    Ellahyani A, El Ansari M, El Jaafari I, CHARFI S (2016) Traffic sign detection and recognition using features combination and random forests. Int J Adv Comput Sci Appl 7(1):686–693Google Scholar
  39. 39.
    Oliveira RB, Pereira AS, Tavares JMRS (2018) Computational diagnosis of skin lesions from dermoscopic images using combined features. Neural Comput Appl. Google Scholar
  40. 40.
    Oliveira RB, Papa JP, Pereira AS, Tavares JMR (2018) Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput Appl 29(3):613–636CrossRefGoogle Scholar
  41. 41.
    Ma Z, Tavares JMR (2017) Effective features to classify skin lesions in dermoscopic images. Expert Syst Appl 84:92–101CrossRefGoogle Scholar
  42. 42.
    Larsson F, Felsberg M (2011) Using Fourier descriptors and spatial models for traffic sign recognition. In: Scandinavian conference on image analysis. Springer, Berlin, pp 238–249Google Scholar
  43. 43.
    Mogelmose A, Trivedi MM, Moeslund TB (2012) Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans Intell Transp Syst 13(4):1484–1497CrossRefGoogle Scholar
  44. 44.
    Grigorescu C, Petkov N (2003) Distance sets for shape filters and shape recognition. IEEE Trans Image Process 12(10):1274–1286MathSciNetCrossRefzbMATHGoogle Scholar
  45. 45.
    Belaroussi R, Foucher P, Tarel JP, Soheilian B, Charbonnier P, Paparoditis N (2010) Road sign detection in images: a case study. In: 2010 20th international conference on pattern recognition (ICPR). IEEE, pp 484–488Google Scholar
  46. 46.
    Mogelmose A, Trivedi MM, Moeslund TB (2012, November) Learning to detect traffic signs: comparative evaluation of synthetic and real-world datasets. In 2012 21st international conference on pattern recognition (ICPR). IEEE, pp 3452–3455Google Scholar
  47. 47.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRefGoogle Scholar
  48. 48.
    Li S, Hao F, Li M, Kim HC (2013, May) Medicine rating prediction and recommendation in mobile social networks. In: International conference on grid and pervasive computing. Springer, Berlin, pp 216–223Google Scholar
  49. 49.
    Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333CrossRefGoogle Scholar
  50. 50.
    Guo Y, Wang M, Li X (2017) Application of an improved Apriori algorithm in a mobile e-commerce recommendation system. Ind Manag Data Syst 117(2):287–303CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.LaSTIC Laboratory, Departement of Computer ScienceUniversity of Batna 2FésdisAlgeria

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