Recognition of Traffic Sign Based on Support Vector Machine and Creation of the Indian Traffic Sign Recognition Benchmark

  • Vidyagouri B. Hemadri
  • Umakant P. Kulkarni
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)


Traffic sign recognition, a driver assistance system informs and warns the driver about the status of the road is a challenging issue. Though, a lot of work on this topic has been carried out, but complete benchmark datasets are not freely available for comparison of different approaches. A few databases are available for benchmarking automatic detection of traffic signs. However, there is no database built considering the Indian traffic signs. The road scenarios in India are quite different from other countries, especially in rural areas. Hence, an effort to build an Indian traffic sign database considering both rural and urban situations is presented in the work. The database consists of 13000 traffic sign images of 50 different classes of traffic signs taken at different times under different environmental conditions and includes the detailed annotation of the traffic signs in terms of size, type, orientation, illumination and occlusion. The work also discusses an efficient method for identification of road signs based on two modules: (1) feature extraction based on dense scale invariant feature transform (DSIFT) and (2) a classifier trained by support vector machine (SVM). The SIFT approach transforms an image it into a large collection of local feature vectors invariant to scaling, translation or rotation of the image, and reduction in the dimensionality is achieved by applying principal component analysis (PCA). After extracting the features, the image is classified using support vector machine, a supervised learning model.


Dense scale invariant feature transform Pattern recognition Principal component analysis Support vector machine 



This work was carried under Research Promotion Scheme grant from All India Council for Technical Education (AICTE), project Ref. No: 8023/RID/RPS-114(Pvt)/2011–12. Authors wish to thank AICTE, New Delhi.


  1. 1.
    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). Scholar
  2. 2.
    Larsson, F., Felsberg, M.: Using fourier descriptors and spatial models for traffic sign recognition. In: SCIA, vol. 11, pp. 238–249 (2011).
  3. 3.
    Timofte, R., Zimmermann, K., VanGool, L.: Multi-view traffic sign detection, recognition, and 3D localisation. Mach. Vis. Appl. 25(3), 633–647 (2011). Scholar
  4. 4.
    Ruta, A., Li, Y., Liu, X.: Real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recogn. 43(1), 416–430 (2010). Scholar
  5. 5.
    Mogelmose, A., Trivedi, M.M., Moeslund, T.B.: Learning to detect traffic signs: comparative evaluation of synthetic and real-world datasets. In: 21st International Conference on Pattern Recognition (ICPR), pp. 3452–3455. IEEE (2012)Google Scholar
  6. 6.
    Fleyeh, H., Davami, E.: Eigen-based traffic sign recognition. IET Intell. Transp. Syst. 5(3), 190–196 (2011). Scholar
  7. 7.
    Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., Hu, S.: Traffic-sign detection and classification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2110–2118 (2016)Google Scholar
  8. 8.
    Yang, Y., Luo, H., Xu, H., Wu, F.: Towards real-time traffic sign detection and classification. IEEE Trans. Intell. Transp. Syst. 17, 2022–2031 (2016). Scholar
  9. 9.
    Soilan, M., Riveiro, B., Martinez-Sánchez, J., Arias, P.: Traffic sign detection in MLS acquired point clouds for geometric and image-based semantic inventory. ISPRS J. Photogrammetry Remote Sens. 114, 92–101 (2016). Scholar
  10. 10.
    Yu, Y., Li, J., Wen, C., Guan, H., Luo, H., Wang, C.: Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data. ISPRS J. Photogrammetry Remote Sens. 113, 106–123 (2016). Scholar
  11. 11.
    Zang, D., Zhang, J., Zhang, D., Bao, M., Cheng, J., Tang, K.: Traffic sign detection based on cascaded convolutional neural networks. In: 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 201–206. IEEE (2016).
  12. 12.
    Qian, R., Liu, Q., Yue, Y., Coenen, F., Zhang, B.: Road surface traffic sign detection with hybrid region proposal and fast R-CNN. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 555–559. IEEE (2016).
  13. 13.
    Yuan, Y., Xiong, Z., Wang, Q.: An incremental framework for video-based traffic sign detection, tracking, and recognition. IEEE Trans. Intell. Transp. Syst. 18(7), 1918–1929 (2017). Scholar
  14. 14.
    Zeng, Y., Xu, X., Shen, D., Fang, Y., Xiao, Z.: Traffic sign recognition using kernel extreme learning machines with deep perceptual features. IEEE Trans. Intell. Transp. Syst. 18(6), 1647–1653 (2017). Scholar
  15. 15.
    Abedin, Z., Dhar, P., Hossenand, M.K., Deb, K.: Traffic sign detection and recognition using fuzzy segmentation approach and artificial neural network classifier respectively. In: International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 518–523. IEEE (2017).
  16. 16.
    Berkaya, S.K., Gunduz, H., Ozsen, O., Akinlar, C., Gunal, S.: On circular traffic sign detection and recognition. Expert Syst. Appl. 48, 67–75 (2016). Scholar
  17. 17.
    Ellahyani, A., El Ansari, M., El Jaafari, I.: Traffic sign detection and recognition based on random forests. Appl. Soft Comput. 46, 805–815 (2016). Scholar
  18. 18.
    Zhu, Y., Zhang, C., Zhou, D., Wang, X., Bai, X., Liu, W.: Traffic sign detection and recognition using fully convolutional network guided proposals. Neurocomputing 214, 758–766 (2016). Scholar
  19. 19.
    Huang, Z., Yu, Y., Gu, J., Liu, H.: An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans. Cybern. 47(4), 920–933 (2017). Scholar
  20. 20.
    Zaklouta, F., Stanciulescu, B.: Real-time traffic sign recognition in three stages. Rob. Auton. Syst. 62(1), 16–24 (2014). Scholar
  21. 21.
    Vahid, B., Arash, J., Saha, G.M.: Multi-class US traffic signs 3D recognition and localization via image-based point cloud model using color candidate extraction and texture-based recognition. Adv. Eng. Inform. 32, 263–274 (2017). Scholar
  22. 22.

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.SDMCETDharwadIndia

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