Abstract
Accurate and up-to-date inventories of traffic signs contribute to efficient road maintenance and a high road safety. This paper describes a system for the automated surveying of road signs from street-level images. This is an extremely challenging task, as the involved capturings are non-densely sampled, captured under a wide range of weather conditions and signs may be distorted. The described system is designed in a generic and learning-based fashion, which enables the recognition of different sign appearance classes with the same algorithms, based on class-specific training data. The system starts with detection of the signs visible within each image, using a detection cascade. Next, the 3D position of the signs that are detected consequently within consecutive capturings is calculated. Afterwards, each positioned road sign is classified to retrieve its sign type, thereby exploiting all detections used during positioning of the respective sign. The presented system is intended for large-scale application and currently supports 11 sign appearance classes, containing 176 different sign types. Performance evaluations conducted on a large, real-world dataset (68,010 images) show that our approach accurately positions 95.5 % of the 3,385 present signs, where 96.3 % of them are also correctly classified. Furthermore, our system localized 98.5 % of the signs in at least a single image. Our system design allows for appending a limited manual correction stage to attain a very high performance, so that sign inventories can be created cost effectively.
Similar content being viewed by others
References
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. SODA ’07, pp. 1027–1035. Society for Industrial and Applied Mathematics, Philadelphia (2007)
Bonaci, I., Kusalic, I., Kovacek, I., Kalafatic, Z., Segvic, S.: Addressing false alarms and localization inaccuracy in traffic sign detection and recognition. In: Proceeding of the 16th Computer Vision Winter Workshop, pp. 1–8 (2011)
Burghouts, G.J., Geusebroek, J.M.: Performance evaluation of local colour invariants. Comput. Vis. Image Underst. 113, 48–62 (2009)
Comanicu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002)
Creusen, I., Hazelhoff, L., de With, P.: Color transformation for improved traffic sign detection. In: Proceeding of the 2012 19th IEEE International Conference on Image Processing (ICIP), pp. 461–464 (2012). doi:10.1109/ICIP.2012.6466896
Creusen, I., Wijnhoven, R.G.J., Herbschleb, E., de With, P.: Color exploitation in hog-based traffic sign detection. In: Proceeding of the 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 2669–2672 (2010). doi:10.1109/ICIP.2010.5651637
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proceeding of the European Conference on Computer Vision (ECCV) (2004)
Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: Proceeding of the IEEE Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893 (2005)
Hazelhoff, L., Creusen, I., de With, P.: Robust classification of traffic signs using multi-view cues. In: Proceeding of the 2012 19th IEEE International Conference on Image Processing (ICIP), pp. 457–460 (2012). doi:10.1109/ICIP.2012.6466895
Hazelhoff, L., Creusen, I.M., De With, P.H.N.: Robust detection, classification and positioning of traffic signs from street-level panoramic images for inventory purposes. In: Proceeding of the Workshop on Applications of Computer Vision (WACV), pp. 313–320 (2012)
Hazelhoff, L., Creusen, I.M., van de Wouw, D.W.J.M., de With, P.H.N.: Large-scale classification of traffic signs under real-world conditions. In: Proceeding of the SPIE 8304B–34 (2012)
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: Proceeding of the International Joint Conference on Neural Networks, p. 1288 (2013)
Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: ICCV, pp. 604–610 (2005)
Lafuente-Arroyo, S., Maldonado-Bascon, S., Gil-Jimenez, P., Acevedo-Rodriguez, J., Lopez-Sastre, R.: A tracking system for automated inventory of road signs. In: Proceeding of the 2007 IEEE Intelligent Vehicles Symposium, pp. 166–171 (2007). doi:10.1109/IVS.2007.4290109
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. (IJCV) 60(2) (2004)
Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gomez-Moreno, H., Lopez-Ferreras, F.: Road-sign detection and recognition based on support vector machines. IEEE Trans. Intell. Transp. Syst. 8(2), 264–278 (2007). doi:10.1109/TITS.2007.895311
Mathias, M., Timofte, R., Benenson, R., Gool, L.J.V.: Traffic sign recognition—how far are we from the solution? In: IJCNN, pp. 1–8. IEEE, New York (2013)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005). doi:10.1109/TPAMI.2005.188
Mogelmose, A., Trivedi, M., Moeslund, T.: Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans. Intell. Transp. Syst. 13(4), 1484–1497 (2012). doi:10.1109/TITS.2012.2209421
Overett, G., Petersson, L.: Large scale sign detection using hog feature variants. In: Proceeding of the 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 326–331 (2011). doi:10.1109/IVS.2011.5940549
Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Netw. 0 (2012). doi:10.1016/j.neunet.2012.02.016
Thain, D., Tannenbaum, T., Livny, M.: Distributed computing in practice: the condor experience. Concurr. Pract. Exp. 17(2–4), 323–356 (2005)
Timofte, R., Gool, L.V.: Sparse representation based projections. In: Proceedings of the British Machine Vision Conference, pp. 61.1–61.12. BMVA Press, USA (2011). http://dx.doi.org/10.5244/C.25.61
Timofte, R., Zimmermann, K., Van Gool, L.: Multi-view traffic sign detection, recognition, and 3d localisation. In: Proceeding of the 2009 Workshop on Applications of Computer Vision (WACV), pp. 1–8 (2009). doi:10.1109/WACV.2009.5403121
Timofte, R., Zimmermann, K., Van Gool, L.: Multi-view traffic sign detection, recognition, and 3d localisation. Mach. Vis. Appl. (2011). doi:10.1007/s00138-011-0391-3
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hazelhoff, L., Creusen, I.M. & de With, P.H.N. Exploiting street-level panoramic images for large-scale automated surveying of traffic signs. Machine Vision and Applications 25, 1893–1911 (2014). https://doi.org/10.1007/s00138-014-0628-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-014-0628-z