Abstract
Automatic traffic sign detection is important in many applications such as GPS based navigation systems, advanced driver assistance systems, and self-driving cars. Recently, several researches have shown that bag of visual words (BoVW) method is really an interesting and potential choice for this detection problem. However, it is difficult for using this approach in practice due to the high computational cost. To find the exact boundaries of objects, this approach has to scan a large number of image sub-windows over location and scale (e.g. there are approximately 60,000 32x32 pixels sub-windows for an 320x240 pixels image). In this paper, we propose an efficient approach, which use multi-scales SIFT features and coarse-to-fine search strategy, to improve speed of BoVW. We argue that multi-scales SIFT features can be used for quickly detecting the coarse boundaries of objects. Then, the further searching stage only need to concentrate on these discovered boundaries. By this way, the number of image sub-windows is efficiently reduced. The experimental results show that our proposed method significantly improves detection speed without trading off performance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Piccioli, G., De Micheli, E., Campani, M.: A robust method for road sign detection and recognition. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 493–500. Springer, Heidelberg (1994)
Barnesi, N., Loy, G., Shaw, D., Robles-Kelly, A.: Regular polygon detection. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 778–785. IEEE (2005)
Ruta, A., Li, Y., Liu, X.: Towards real-time traffic sign recognition by class-specific discriminative features. In: BMVC, pp. 1–10 (2007)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, vol. 1, p. 22 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)
Zhang, J., Marszałek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision 73, 213–238 (2007)
Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond sliding windows: Object localization by efficient subwindow search. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Zelnik-Manor, L.: On sifts and their scales. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1522–1528. IEEE Computer Society (2012)
Bosch, A., Zisserman, A., Muoz, X.: Image classification using random forests and ferns. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)
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: International Joint Conference on Neural Networks (submitted, 2013)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian detection at 100 frames per second. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2903–2910. IEEE (2012)
Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: BMVC, vol. 2, p. 5 (2009)
Aldavert, D., Ramisa, A., de Mantaras, R.L., Toledo, R.: Fast and robust object segmentation with the integral linear classifier. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1046–1053. IEEE (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nguyen, KD., Le, DD., Duong, D.A. (2013). Efficient Traffic Sign Detection Using Bag of Visual Words and Multi-scales SIFT. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-42051-1_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
eBook Packages: Computer ScienceComputer Science (R0)