Framework for Machine Vision Based Traffic Sign Inventory

  • Petri Hienonen
  • Lasse Lensu
  • Markus Melander
  • Heikki Kälviäinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10269)


Automatic traffic sign inventory and simultaneous condition analysis can be used to improve road maintenance processes, decrease maintenance costs, and produce up-to-date information for future intelligent driving systems. The goal of this research is to combine automatic traffic sign detection and classification with traffic sign inventory and condition analysis. This paper proposes a complete machine vision framework for the purpose and presents the results of its performance evaluation with three datasets: Traffic Signs Dataset, and two datasets collected for this research. The experimental results show that the system is able to detect, locate, and classify almost all the traffic signs, and is a suitable platform for traffic sign condition analysis.


Traffic sign inventory Detection Classification Localization Distributed asset management Condition analysis Machine vision Image processing 



The authors would like to thank the Finnish Transport Agency for funding of the TrafficVision research project.


  1. 1.
    Hienonen, P.: Automatic traffic sign inventory and condition analysis. Master’s thesis, Lappeenranta University of Technology, Finland (2014)Google Scholar
  2. 2.
    Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. NN 32, 323–332 (2012)Google Scholar
  3. 3.
    Baro, X., Escalera, S., Vitria, J., Pujol, O., Radeva, P.: Traffic sign recognition using evolutionary adaboost detection and forest-ECOC classification. IEEE ITS 10(1), 113–126 (2009)Google Scholar
  4. 4.
    Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., Igel, C.: Detection of traffic signs in real-world images: the German traffic sign detection benchmark. In: Proceedings of IJCNN (2013)Google Scholar
  5. 5.
    Mogelmose, A., Trivedi, M., Moeslund, T.: Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE ITS 13, 1484–1497 (2012)Google Scholar
  6. 6.
    Bahlmann, C., Zhu, Y., Ramesh, V., Pellkofer, M., Koehler, T.: A system for traffic sign detection, tracking, and recognition using color, shape, and motion information. In: Proceedings of IEEE IV (2005)Google Scholar
  7. 7.
    Maldonado-Bascon, S., Lafuente-Arroyo, S., P. Siegmann, H.G.M., Acevedo-Rodrıguez, F.: Traffic sign recognition system for inventory purposes. In: Proceedings of IEEE IVS (2008)Google Scholar
  8. 8.
    Hazelhoff, L., Creusen, I.M.: Exploiting street-level panoramic images for large-scale automated surveying of traffic signs. MVA 25, 1893–1911 (2014)Google Scholar
  9. 9.
    Timofte, R., Zimmermann, K., Van Gool, L.: Multi-view traffic sign detection, recognition, and 3D localisation. MVA 25, 633–647 (2011)Google Scholar
  10. 10.
    Larsson, F., Felsberg, M.: Using Fourier descriptors and spatial models for traffic sign recognition. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 238–249. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21227-7_23 CrossRefGoogle Scholar
  11. 11.
    Larsson, F., Felsberg, M., Forssen, P.E.: Correlating Fourier descriptors of local patches for road sign recognition. IET Comput. Vis. 5, 244–254 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Gu, Y., Yendo, T., Tehrani, M., Fujii, T., Tanimoto, M.: Traffic sign detection in dual-focal active camera system. In: Proceedings of IEEE IV (2011)Google Scholar
  13. 13.
    Gonzalez, A., Garrido, M., Llorca, D., Gavilan, M., Fernandez, J., Alcantarilla, P., Parra, I., Herranz, F., Bergasa, L., Sotelo, M., Revenga de Toro, P.: Automatic traffic signs and panels inspection system using computer vision. IEEE ITS 12, 485–499 (2011)Google Scholar
  14. 14.
    Houben, S.: A single target voting scheme for traffic sign detection. In: Proceedings of IEEE IV (2011)Google Scholar
  15. 15.
    Fleyeh, H.: Color detection and segmentation for road and traffic signs. In: Proceedings of IEEE CIS (2004)Google Scholar
  16. 16.
    Viola, P., Jones, M.J.: Robust real-time face detection. IJCV 57, 137–154 (2004)CrossRefGoogle Scholar
  17. 17.
    Zaklouta, F., Stanciulescu, B., Hamdoun, O.: Traffic sign classification using k-d trees and random forests. In: Proceedings of IJCNN (2011)Google Scholar
  18. 18.
    Mathias, M., Timofte, R., Benenson, R., Gool, L.J.V.: Traffic sign recognition - how far are we from the solution? In: Proceedings of IJCNN (2013)Google Scholar
  19. 19.
    Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE PAMI 23, 228–233 (2001)CrossRefGoogle Scholar
  20. 20.
    Zhu, Y., Wang, X., Yao, C., Bai, X.: Traffic sign classification using two-layer image representation. In: Proceedings of ICIP (2013)Google Scholar
  21. 21.
    Lu, K., Ding, Z., Ge, S.: Sparse-representation-based graph embedding for traffic sign recognition. IEEE ITS 13, 1515–1524 (2012)Google Scholar
  22. 22.
    de la Escalera, A., Moreno, L., Salichs, M., Armingol, J.: Road traffic sign detection and classification. IEEE IE 44, 848–859 (1997)Google Scholar
  23. 23.
    Fleyeh, H.: Shadow and highlight invariant colour segmentation algorithm for traffic signs. In: Proceedings of CCIS (2006)Google Scholar
  24. 24.
    Broggi, A., Cerri, P., Medici, P., Porta, P., Ghisio, G.: Real time road signs recognition. In: Proceedings of IEEE IV (2007)Google Scholar
  25. 25.
    Dollar, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE PAMI 36, 1532–1545 (2014)CrossRefGoogle Scholar
  26. 26.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE PAMI 34, 743–761 (2012)CrossRefGoogle Scholar
  27. 27.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefzbMATHGoogle Scholar
  28. 28.
    Dollar, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: Proceedings of BMVC (2009)Google Scholar
  29. 29.
    Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: Proceedings of IJCNN (2011)Google Scholar
  30. 30.
    Karney, C.F.: Algorithms for geodesics. J. Geod. 87, 43–55 (2013)CrossRefGoogle Scholar
  31. 31.
    Martí, E.D., Martín, D., García, J., de la Escalera, A., Molina, J.M., Armingol, J.M.: Context-aided sensor fusion for enhanced urban navigation. Sensors 12, 16802–16837 (2012)CrossRefGoogle Scholar
  32. 32.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Petri Hienonen
    • 1
    • 2
  • Lasse Lensu
    • 1
  • Markus Melander
    • 2
    • 3
  • Heikki Kälviäinen
    • 1
  1. 1.Machine Vision and Pattern Recognition Laboratory (MVPR), School of Engineering ScienceLappeenranta University of Technology (LUT)LappeenrantaFinland
  2. 2.Vionice Ltd.LappeenrantaFinland
  3. 3.Finnish Transport Agency (FTA)LappeenrantaFinland

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