Skip to main content
SpringerLink
Log in

Indian Traffic Sign Detection and Recognition

  • Published:
International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

Traffic Sign Recognition system is a very significant part of the Intelligent Transportation System, as traffic signs assist the drivers to drive more carefully and professionally. The main aim of this work is to present an efficient approach for detection and recognition of Indian traffic signs. Information regarding color and geometrical shape of traffic signs are utilized by the system for localizing the traffic sign in the acquired image. An RGB color saliency attention model of traffic sign makes use of an algorithm, which discriminates the sign candidate from other objects. Morphological shape filter is exploited for extracting the geometrical information of the traffic sign. Nearest neighbor matching-based recognition is performed between localized candidate features and stored Indian traffic sign database (ITSD) features. Speed up robust features (SURF) of a traffic sign is used in nearest neighbor matching to find out the resemblance between the traffic signs. System robustness is cross-examined for illumination, scale, rotation variations, similar color and shape variations, a standard data set is also considered to evaluate the system performance. The simulation results illustrate that the proposed system is working effectively under various hazardous condition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Handmann, U., Kalinke, T., Tzomakas, C., Werner, M., Seelen, W.: An image processing system for driver assistance. Image Vis. Comput. 18(5), 367–376 (2000)

    Article  Google Scholar 

  2. Timofte, R., et al.: Combining traffic sign detection with 3D tracking towards better driver assistance. Emerg. Top. Comp. Vis. and its App. 1–22 (2011)

  3. Swathi, M., et al.: Automatic traffic sign detection and recognition: a review. International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), pp. 1–17 (2017)

  4. Escalera, S., et al.: Background on traffic sign detection and recognition. Traffic-sign recognition systems, pp. 5–13. Springer (2011)

  5. Yakimov et al.: Traffic signs detection using tracking with prediction. International conference on E-business and telecommunications Colmar, Springer, 454–467 (2015)

  6. Brkic et al.: An overview of traffic sign detection methods. Department of Electronics, microelectronics, Computer and Intelligent Systems Faculty of Electrical Engineering and Computing, 1–9 (2010)

  7. Saadna, Y., Behloul, A.: An overview of traffic sign detection and classification methods. Int. J. Multimed. Inf. Retr. 6(3), 193–210 (2017)

    Article  Google Scholar 

  8. H. Kamada et al.: A compact navigation system using image processing and fuzzy control,” In Proceeding IEEE South east con, 337–342(1990)

  9. R. Janssen et al.: Hybrid approach for traffic sign recognition. Proc. IEEE Int. Conference Intel. Vehicles, 390–397 (1993)

  10. C. Bahlmannetal.: A system for traffic sign detection, tracking, and recognition using color, shape, and motion information. IEEE Proceedings. Intel. Veh., 255–260 (2005)

  11. S. Maldonado-Bascónet et al.: Road-sign detection and recognition based on support vector machines. IEEE Tran. On Intel. Transp. System, pp. 264–278, 2007

  12. Malik, R., et al.: Road sign detection and recognition using color segmentation, shape analysis and template matching. Proc. Mach. Learn. Cybern. 3556–3560 (2007)

  13. H. Huang et al.: Automatic detection and recognition of road sign. Int. Con. Mech. Embed. Syst. Appl., 626–630 (2008)

  14. C. G. Kiran et al.: Traffic sign detection and pattern recognition using support vector machine. Int. Con. Adv. Pat. Recogn. 87–90 (2009)

  15. S. Vitabile et al.: Road signs recognition using a dynamic pixel aggregation technique in the HSV color space. Proc. Image Anal. Process 572–577 (2001)

  16. P. Wanitchai et al.: Traffic warning signs detection and recognition based on fuzzy logic and chain code analysis. Int. Symp. Intel. Inf. Tech. Appl. 508–512 (2008)

  17. W.Shadeed, et al.: Road traffic sign detection in color images. Int. Con. Elect. Cir. Syst. 890–893 (2003)

  18. Wen et al.: Image retrieval based on saliency attention. Found. Intell. Syst. 177–188 (2014)

  19. P. Paclik et al.: Road sign classification without color information. Proc. Con. Adv. School Imaging Comput. 1–7 (2000)

  20. G. Loy et al.: Fast shape-based road sign detection for a driver assistance system. Int. Con. Intell. Robot Syst. 70–75 (2004)

  21. Hechri et al.: A robust road lanes and traffic signs recognition for driver assistance system. Int. J. Comp. Sci. Eng. 202–209 (2015)

    Article  Google Scholar 

  22. Garcia-Garrido MA et al.: Fast traffic sign detection and recognition under changing lighting conditions. IEEE Intel. Trans. Syst 811–816 (2006)

  23. Borji, et al.: Online learning of task-driven object-based visual attention control. Image Vis. Comput. 28(7), 1130–1145 (2010)

    Article  Google Scholar 

  24. Ruta, et al.: A.: real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recogn. 43(1), 416–−430 (2010)

    Article  Google Scholar 

  25. Creusen et al.: Color exploitation in hog-based traffic sign detection. IEEE Int. Con. Image Process 2669–2672 (2010)

  26. Overett et al.: Large scale sign detection using HOG feature variants. IEEE Intel. Veh. Symp. 326–331 (2011)

  27. “Volkswagen Media Service phaeton debuts with new design and new technologies” https://www.volkswagenmediaservice.com/media_publish/ms/content/en/pressemitteilungen. Accessed 22 April 2010

  28. Mobil eye Traffic Sign Detection: http://www.mobileye.com/technology/applications/traffic-sign-detection/. Accessed 26 Oct 2011

  29. Devpriya et al.: Indian traffic sign recognition using HSV color model and kernel extreme learning machine. Int. J. Print Packag. Allied Sci. 3381–3391(2016)

  30. Huda noor A et al.: Real time detection and recognition of Indian traffic sign using Matlab. Int. J. Sci. Eng. Res. 684–690 (2013)

  31. Arun nandewal et al.: Indian traffic sign detection and classification using neural network. Int. Congr. Technol. Manag. Soc. Sci. 11–17 (2016)

  32. Jacob Toft Pedersen: Study group SURF: Feature detection &description: http://cs.au.dk/~jtp/SURF/report.pdf. Accessed 20 Feb 2018

  33. Indian traffic sign: https://www.mapsofindia.com/my-india/government/traffic-signs-and-road-safety. Accessed 20 Feb 2018

  34. Itti, et al.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  35. Lasota M et al.: Recognition of multiple traffic signs using key points feature detectors. Int. Conf. Expo. Electr. Power Eng. 535–540 (2016)

  36. Yakimov, P.Y., et al.: Pre-processing digital images for quickly and reliably detecting road signs. Pattern Recognit. Image Anal. 25(4), 729–732 (2015)

    Article  Google Scholar 

  37. Herbet bay, et al.: SURF: speed up robust feature, pp. 404–417. Springer-Verlag Berlin, Heidelberg (2006)

    Google Scholar 

  38. Gomez-Moreno, H., Maldonado-Bascon, S., Gil-Jimenez, P., Lafuente-Arroyo, S.: Goal evaluation of segmentation algorithms for traffic sign recognition. IEEE Trans. Intel. Trans. Syst. 11(4), 917–930 (2010)

    Article  Google Scholar 

  39. R Gonzalez: Digital image processing using MATLAB. Third edition, paperback, 2017

  40. Youssef A et al.: Fast Traffic Sign Recognition Using Color Segmentation and Deep Convolution Networks. International Conference on Advanced Concepts for Intelligent Vision Systems, Springer, 205–216 (2016)

Download references

Acknowledgements

The authors are thankful to the minority affairs, Govt. of India for providing the fellowship (MANF) for this research work.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India

    Altaf Alam & Zainul Abdin Jaffery

Authors
  1. Altaf Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zainul Abdin Jaffery
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Altaf Alam.

Ethics declarations

Conflict of Interest

The authors, Altaf Alam and Zainul Abdin Jaffery, declare that they have no conflict of interest relating to this work and publication.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alam, A., Jaffery, Z.A. Indian Traffic Sign Detection and Recognition. Int. J. ITS Res. 18, 98–112 (2020). https://doi.org/10.1007/s13177-019-00178-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-019-00178-1

Keywords

Search

Navigation