Skip to main content
SpringerLink
Log in

Fast-Tracking Application for Traffic Signs Recognition

  • Conference paper
  • First Online:
Computer Vision and Graphics (ICCVG 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11114))

Included in the following conference series:

Abstract

Traffic sign recognition is among the major tasks on driver assistance system. The convolutional neural networks (CNN) play an important role to find a good accuracy of traffic sign recognition in order to limit the dangerous acts of the driver and to respect the road laws. The accuracy of the Detection and Classification determines how powerful of the technique used is. Whereas SSD Multibox (Single Shot MultiBox Detector) is an approach based on convolutional neural networks paradigm, it is adopted in this paper, firstly because we can rely on it for the real-time applications, this approach runs on 59 FPS (frame per second). Secondly, in order to optimize difficulties in multiple layers of DeeperCNN to provide a finer accuracy. Moreover, our experiment on German traffic sign recognition benchmark (GTSRB) demonstrated that the proposed approach could achieve competitive results (83.2% in 140.000 learning steps) using GPU parallel system and Tensorflow.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Speed limit 20 km/h, Speed limit 30 km/h, Speed limit 50 km/h, Speed limit 60 km/h, Speed limit 70 km/h, Speed limit 80 km/h, End of speed limit 80 km/h, Speed limit 100 km/h, Speed limit 120 km/h, No overtaking, No overtaking by lorries, Junction with minor roads, Main road, Give way, Stop and give way, No entry for vehicles (both directions), No lorries, No entry for vehicles, Other hazard, Curve to the left, Curve to the right, Double curve, first to the left, Bumpy road, Danger of skidding, Road narrows (right side), Roadworks, Traffic lights ahead, Caution for pedestrians, Caution school, Caution for bicyclists, Be careful in winter, Wild animals, End of all prohibitions, Turn right ahead, Turn left ahead, Ahead only, Ahead or right only, Ahead or left only, Keep right, Keep left, Roundabout, End of no-overtaking zone, End of no-overtaking zone for lorrie.

References

  1. Shustanov, A., Yakimov, P.: CNN design for real-time traffic sign recognition. Procedia Eng. 201, 718–725 (2017)

    Article  Google Scholar 

  2. Caglayan, A., Can, A.B.: An empirical analysis of deep feature learning for RGB-D object recognition. In: Karray, F., Campilho, A., Cheriet, F. (eds.) ICIAR 2017. LNCS, vol. 10317, pp. 312–320. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59876-5_35

    Chapter  Google Scholar 

  3. Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649, June 2012. https://doi.org/10.1109/CVPR.2012.6248110

  4. Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2147–2154 (2014)

    Google Scholar 

  5. Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, June 2008. https://doi.org/10.1109/CVPR.2008.4587597

  6. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, pp. 580–587. IEEE Computer Society, Washington, DC (2014). http://dx.doi.org/10.1109/CVPR.2014.81

  7. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385

  8. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV 2015, pp. 1026–1034. IEEE Computer Society, Washington, DC (2015). http://dx.doi.org/10.1109/ICCV.2015.123

  9. Hosang, J.H., Benenson, R., Schiele, B.: How good are detection proposals, really? CoRR abs/1406.6962 (2014). http://arxiv.org/abs/1406.6962

  10. Kosub, S.: A note on the triangle inequality for the Jaccard distance, December 2016

    Google Scholar 

  11. Lampert, C., Blaschko, M., Hofmann, T.: Beyond sliding windows: object localization by efficient subwindow search. In: CVPR 2008, pp. 1–8. Max-Planck-Gesellschaft, IEEE Computer Society, Los Alamitos, June 2008. Best paper award

    Google Scholar 

  12. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  13. Martinović, A., Glavaš, G., Juribašić, M., Sutić, D., Kalafatić, Z.: Real-time detection and recognition of traffic signs. In: The 33rd International Convention MIPRO, pp. 760–765, May 2010

    Google Scholar 

  14. y. Nguwi, Y., Kouzani, A.Z.: A study on automatic recognition of road signs. In: 2006 IEEE Conference on Cybernetics and Intelligent Systems, pp. 1–6, June 2006. https://doi.org/10.1109/ICCIS.2006.252289

  15. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, June 2016. https://doi.org/10.1109/CVPR.2016.91

  16. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 91–99. Curran Associates, Inc. (2015). http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf

  17. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014). http://arxiv.org/abs/1409.1556

  18. Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: IEEE International Joint Conference on Neural Networks, pp. 1453–1460 (2011)

    Google Scholar 

  19. Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015. DOI: https://doi.org/10.1109/CVPR.2015.7298594

  20. Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013). https://doi.org/10.1007/s11263-013-0620-5

    Article  Google Scholar 

  21. Zaklouta, F., Stanciulescu, B.: Real-time traffic sign recognition in three stages. Robot. Auton. Syst.62(1), 16–24 (2014). https://doi.org/10.1016/j.robot.2012.07.019, http://www.sciencedirect.com/science/article/pii/S0921889012001236 new Boundaries of Robotics

    Article  Google Scholar 

  22. Zeng, Y., Xu, X., Fang, Y., Zhao, K.: Traffic sign recognition using deep convolutional networks and extreme learning machine. In: He, X., et al. (eds.) IScIDE 2015. LNCS, vol. 9242, pp. 272–280. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23989-7_28

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Faculty of Science, LR2I, FSAC, Hassan II University of Casablanca, B.P 5366, Maarif, Casablanca, Morocco

    Abderrahmane Adoui El Ouadrhiri, Said Jai Andaloussi, Ouail Ouchetto & Abderrahim Sekkaki

  2. Department of Geoinformatics, Faculty of Science, KGI, Palacky University, 17. listopadu 50, 771 46, Olomouc, Czech Republic

    Abderrahmane Adoui El Ouadrhiri & Jaroslav Burian

  3. Department of Geography, Faculty of Arts and Humanities LADES, CERES, FLSH-M, Hassan II University of Casablanca, B.P 546, Mohammedia, Morocco

    Rachida El Morabet

Authors
  1. Abderrahmane Adoui El Ouadrhiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaroslav Burian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Said Jai Andaloussi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rachida El Morabet
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ouail Ouchetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Abderrahim Sekkaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abderrahmane Adoui El Ouadrhiri .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Leszek J. Chmielewski

  2. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Ryszard Kozera

  3. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Arkadiusz Orłowski

  4. Institute of Computer Science, Silesian University of Technology, Gliwice, Poland

    Konrad Wojciechowski

  5. Technion, Israel Institute of Technology, Haifa, Israel

    Alfred M. Bruckstein

  6. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

El Ouadrhiri, A.A., Burian, J., Andaloussi, S.J., El Morabet, R., Ouchetto, O., Sekkaki, A. (2018). Fast-Tracking Application for Traffic Signs Recognition. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00692-1_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00691-4

  • Online ISBN: 978-3-030-00692-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation