An efficient traffic sign recognition based on graph embedding features


Traffic sign recognition (TSR) is one of the significant modules of an intelligent transportation system. It instantly assists the drivers to efficiently recognize the traffic sign. Recognition of traffic sign is a large-scale feature learning problem with different real-world appearances. The main goal of this paper is to develop an efficient TSR method, which can run on an ordinary personal computer (PC). In the proposed method, GIST descriptors of the traffic sign images are extracted and subjected to graph-based linear discriminant analysis to reduce the dimension. Moreover, it effectively learns the discriminative subspace through the graph structure with increased computational efficiency. An efficient TSR module is built by conducting series of experiments using support vector machine, extreme learning machine, and k-nearest neighbor (k-NN) classifiers on available public datasets. Our approach achieved the highest recognition accuracy of 96.33 and 97.79% using k-NN classifier for German Traffic Sign Recognition Benchmark (GTSRB) and Belgium Traffic Sign Classification Benchmark (BelgiumTSC), respectively. Also it achieved 99.1% accuracy for a subcategory of GTSRB traffic signs and able to predict the class of unknown traffic sign within 0.0019 s on an ordinary PC. Hence, it can be used in real-world driver assistance system.

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  1. 1.

  2. 2.

  3. 3.


  1. 1.

    Acharya UR, Fujita H, Bhat S, Raghavendra U, Gudigar A, Molinari F, Vijayananthan A, Ng KH (2016) Decision support system for fatty liver disease using gist descriptors extracted from ultrasound images. Inf Fusion 29:32–39

    Article  Google Scholar 

  2. 2.

    Alsibai M, Hirai Y (2010) Real-time recognition of blue traffic signs designating directions. Int J Intell Transp Syst Res 8(2):96–105

    Google Scholar 

  3. 3.

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

    Article  Google Scholar 

  4. 4.

    Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396

    MATH  Article  Google Scholar 

  5. 5.

    Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167

    Article  Google Scholar 

  6. 6.

    Cai D, He X, Zhou K, Han J, Bao H (2007) Locality sensitive discriminant analysis. In: Proceedings of the 20th international joint conference on artificial intelligence, Hyderabad, pp 708–713

  7. 7.

    Cai D, He X, Han J (2008) Srda: an efficient algorithm for large-scale discriminant analysis. IEEE Trans Knowl Data Eng 20(1):1–12

    Article  Google Scholar 

  8. 8.

    Cai Z, Gu M (2013) Traffic sign recognition algorithm based on shape signature and dual-tree complex wavelet transform. J Central South Univ 20(2):433–439

    Article  Google Scholar 

  9. 9.

    Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27.

  10. 10.

    Ciresan D, Meier U, Masci J, Schmidhuber J (2011) A committee of neural networks for traffic sign classification. In: The 2011 international joint conference on neural networks, San Jose, CA, pp 1918–1921

  11. 11.

    Ciresan DC, Meier U, Masci J, Schmidhuber J (2012) Multi-column deep neural network for traffic sign classification. Neural Netw 32:333–338

    Article  Google Scholar 

  12. 12.

    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR’05, pp 886–893

  13. 13.

    Ellahyani A, Ansari ME, Jaafari IE (2016) Traffic sign detection and recognition based on random forests. Appl Soft Comput. doi:10.1016/j.asoc.2015.12.041

    Article  Google Scholar 

  14. 14.

    Escalera S, Pujol O, Radeva P (2010) Traffic sign recognition system with-correction. Mach Vis Appl 21(2):99–111

    Article  Google Scholar 

  15. 15.

    Fleyeh H, Davami E (2011) Eigen-based traffic sign recognition. IET Intell Transp Syst 5(3):190–196

    Article  Google Scholar 

  16. 16.

    Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press, USA

    Google Scholar 

  17. 17.

    Gelman A (2005) Analysis of variance? Why it is more important than ever. Ann Stat 33(1):1–53

    MathSciNet  MATH  Article  Google Scholar 

  18. 18.

    Gil Jiménez P, Bascón SM, Moreno HG, Arroyo SL, Ferreras FL (2008) Traffic sign shape classification and localization based on the normalized fft of the signature of blobs and 2d homographies. Signal Process 88(12):2943–2955

    MATH  Article  Google Scholar 

  19. 19.

    Gonzalez-Reyna SE, Avina-Cervantes JG, Ledesma-Orozco SE, Cruz-Aceves I (2013) Eigen-gradients for traffic sign recognition. Math Probl Eng. doi:10.1155/2013/364305

    Article  Google Scholar 

  20. 20.

    Greenhalgh J, Mirmehdi M (2012) Real-time detection and recognition of road traffic signs. IEEE Trans Intell Transp Syst 13(4):1498–1506

    Article  Google Scholar 

  21. 21.

    Gudigar A, Jagadale BN, Mahesh PK, Raghavendra U (2012) Kernel Based Automatic Traffic Sign Detection and Recognition Using SVM. In: Proceedings of Eco-friendly Computing and Communication Systems: International Conference, ICECCS 2012, Kochi, India, pp 153–161

  22. 22.

    Gudigar A, Chokkadi S, Raghavendra U, Acharya UR (2016) Multiple thresholding and subspace based approach for detection and recognition of traffic sign. Multimedia Tools Appl. doi:10.1007/s11042-016-3321-6

    Article  Google Scholar 

  23. 23.

    Gudigar A, Chokkadi S, Raghavendra U (2016) A review on automatic detection and recognition of traffic sign. Multimedia Tools Appl 75(1):333–364

    Article  Google Scholar 

  24. 24.

    Gudigar A, Chokkadi S, Raghavendra U, Acharya UR (2017) Local texture patterns for traffic sign recognition using higher order spectra. Pattern Recogn Lett. doi:10.1016/j.patrec.2017.02.016

    Article  Google Scholar 

  25. 25.

    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  26. 26.

    Han PY, Jin ATB, Abas FS (2009) Neighbourhood preserving discriminant embedding in face recognition. J Vis Commun Image Represent 20(8):532–542

    Article  Google Scholar 

  27. 27.

    He X, Cai D, Yan S, Zhang HJ (2005) Neighborhood preserving embedding. Proc Tenth IEEE Int Confer Comput Vis Beijing 2:1208–1213

    Google Scholar 

  28. 28.

    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  29. 29.

    Huang Z, Yu Y, Gu J, Liu H (2016) An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans Cybern 99:1–14

    Google Scholar 

  30. 30.

    Huynh-The T, Thanh HN, Cong HT (2014) Traffic sign recognition using multi-class morphological detection. In: International conference on advanced technologies for communications (ATC 2014), Vietnam, pp 274–279

  31. 31.

    Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia, New York, NY, pp 675–678

  32. 32.

    Jin J, Fu K, Zhang C (2014) Traffic sign recognition with hinge loss trained convolutional neural networks. IEEE Trans Intell Transp Syst 15(5):1991–2000

    Article  Google Scholar 

  33. 33.

    Jung S, Lee U, Jung J, Shim DH (2016) Real-time traffic sign recognition system with deep convolutional neural network. In: 13th international conference on ubiquitous robots and ambient intelligence (URAI), pp 31–34

  34. 34.

    Kassani PH, Teoh ABJ (2017) A new sparse model for traffic sign classification using soft histogram of oriented gradients. Appl Soft Comput 52:231–246

    Article  Google Scholar 

  35. 35.

    Khan JF, Bhuiyan SMA, Adhami RR (2011) Image segmentation and shape analysis for road-sign detection. IEEE Trans Intell Transp Syst 12(1):83–96

    Article  Google Scholar 

  36. 36.

    Larsson F, Felsberg M (2011) Using Fourier descriptors and spatial models for traffic sign recognition. In: SCIA, lecture notes in computer science vol 6688, pp 238–249

  37. 37.

    Liu H, Liu Y, Sun F (2014) Traffic sign recognition using group sparse coding. Inf Sci 266:75–89

    Article  Google Scholar 

  38. 38.

    Lu K, Ding Z, Ge S (2012) Sparse-representation-based graph embedding for traffic sign recognition. IEEE Trans Intell Transp Syst 13(4):1515–1524

    Article  Google Scholar 

  39. 39.

    Mathias M, Timofte R, Benenson R, Gool LV (2013) Traffic sign recognition how far are we from the solution? In: The 2013 international joint conference on neural networks. Dallas, pp 1–8

  40. 40.

    Mitchell TM (1997) Machine Learning, 1st edn. McGraw-Hill Inc, New York

    Google Scholar 

  41. 41.

    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):1484–1497

    Article  Google Scholar 

  42. 42.

    Mogelmose A, Trivedi MM, Moeslund TB (2012) Traffic sign detection and analysis: Recent studies and emerging trends. In 15th International IEEE Conference on Intelligent Transportation Systems. USA, pp 1310–1314

  43. 43.

    Nguwi YY, Cho SY (2010) Emergent self-organizing feature map for recognizing road sign images. Neural Comput Appl 19(4):601–615

    Article  Google Scholar 

  44. 44.

    Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    MATH  Article  Google Scholar 

  45. 45.

    Oliva A, Torralba AB, Guérin-Dugué A, Hérault J (1999) Global semantic classification of scenes using power spectrum templates. In: Proceedings of the 1999 international conference on challenge of image retrieval, Swindon, pp 1–12

  46. 46.

    Pazhoumand-dar H, Yaghoobi M (2013) A new approach in road sign recognition based on fast fractal coding. Neural Comput Appl 22(3–4):615–625

    Article  Google Scholar 

  47. 47.

    Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  48. 48.

    Ruta A, Porikli F, Shintaro W, Li Y (2011) In-vehicle camera traffic sign detection and recognition. Mach Vis Appl 22(2):359–375

    Article  Google Scholar 

  49. 49.

    Sermanet P, LeCun Y (2011) Traffic sign recognition with multi-scale convolutional networks. In: The 2011 international joint conference on neural networks, San Jose, pp 2809–2813

  50. 50.

    Siagian C, Itti L (2007) Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans Pattern Anal Mach Intell 29(2):300–312

    Article  Google Scholar 

  51. 51.

    Souani C, Faiedh H, Besbes K (2014) Efficient algorithm for automatic road sign recognition and its hardware implementation. J Real Time Image Process 9(1):79–93

    Article  Google Scholar 

  52. 52.

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

    Article  Google Scholar 

  53. 53.

    Sun ZL, Wang H, Lau WS, Seet G, Wang D (2014) Application of BW-ELM model on traffic sign recognition. Neurocomputing 128:153–159

    Article  Google Scholar 

  54. 54.

    Tenenbaum JB, Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  55. 55.

    Timofte R, Gool LV (2011) Fast approaches to large-scale classification. In: submitted to international joint conference on neural networks

  56. 56.

    Timofte R, Zimmermann K, Gool LJV (2014) Multi-view traffic sign detection, recognition, and 3d localisation. Mach Vis Appl 25(3):633–647

    Article  Google Scholar 

  57. 57.

    Wali SB, Hannan MA, Hussain A, Samad SA (2015) An automatic traffic sign detection and recognition system based on colour segmentation, shape matching, and SVM. Math Probl Eng. doi:10.1155/2015/250461

    Article  Google Scholar 

  58. 58.

    Wang CW, You WH (2013) Boosting-svm: effective learning with reduced data dimension. Appl Intell 39(3):465–474

    Article  Google Scholar 

  59. 59.

    Xu S (2009) Robust traffic sign shape recognition using geometric matching. IET Intell Transp Syst 3(1):10–18

    Article  Google Scholar 

  60. 60.

    Yan S, Xu D, Zhang B, Zhang HJ, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  61. 61.

    Ye J (2005) Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J Mach Learn Res 6:483–502

    MathSciNet  MATH  Google Scholar 

  62. 62.

    Yuan X, Hao X, Chen H, Wei X (2014) Robust traffic sign recognition based on color global and local oriented edge magnitude patterns. IEEE Trans Int Transport Syst 15(4):1466–1477

    Article  Google Scholar 

  63. 63.

    Zaklouta F, Stanciulescu B (2012) Real-time traffic-sign recognition using tree classifiers. IEEE Trans Int Transport Syst 13(4):1507–1514

    Article  Google Scholar 

  64. 64.

    Zaklouta F, Stanciulescu B (2014) Real-time traffic sign recognition in three stages. Robot Auton Syst 62(1):16–24

    Article  Google Scholar 

  65. 65.

    Zaklouta F, Stanciulescu B, Hamdoun O (2011) Traffic sign classification using k-d trees and random forests. In: The 2011 international joint conference on neural networks (IJCNN), USA, pp 2151–2155

  66. 66.

    Zeng Y, Xu X, Fang Y, Zhao K (2015) Traffic sign recognition using deep convolutional networks and extreme learning machine in Intelligence Science and Big Data Engineering. Image and Video Data Engineering. 5th International Conference. IScIDE 2015, Suzhou, China, 14–16 June 2015

  67. 67.

    Zhang K, Sheng Y, Li J (2012) Automatic detection of road traffic signs from natural scene images based on pixel vector and central projected shape feature. IET Intell Transp Syst 6(3):282–291

    Article  Google Scholar 

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Correspondence to Anjan Gudigar.

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See Table 7 and Fig. 7.

Table 7 Finally selected features with its F-value on BelgiumTSC dataset
Fig. 7

Complete description of the proposed TSR module

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Gudigar, A., Chokkadi, S., Raghavendra, U. et al. An efficient traffic sign recognition based on graph embedding features. Neural Comput & Applic 31, 395–407 (2019).

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  • Computer vision
  • Intelligent transportation system
  • Feature selection
  • GIST
  • Real-world driver assistance system
  • Traffic sign recognition