Abstract
The road signs provide important information about road and traffic to drivers for safety driving. These signs include not only common traffic signs but also the information about unexpected obstacles and road constructions. Accurate detection and identification of road signs is one of the research topics in vehicle vision area. In this paper we propose a stereo vision technique to automatically detect and track road signs in a video sequence which is acquired from a stereo vision camera mounted on a vehicle. First, color information is used to initially detect the candidates of road signs. Second, the Support Vector Machine (SVM) is used to select true signs from the candidates. Once a road sign is detected in a video frame, it is tacked from the next frame until disappeared. The 2-D position of the detected sign on the next frame is predicted by the motion of the vehicle. Here, the vehicle motion means the 3-D Euclidean motion acquired by using a stereo matching method. Finally, the predicted 2-D position of the sign is corrected by the template matching of a scaled sign template in the near regions of the predicted position. Experimental results show that the proposed method can detect and track road signs successfully. Error comparisons with two different detection and tracking methods are shown.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Ruta, A., Li, Y., Liu, X.: Detection, Tracking and Recognition of Traffic Signs from Video Input. In: 11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008, October 12-15, pp. 55–60 (2008)
de la Escalera, A., Armingol, J.M., Pastor, J.M., Rodriguez, F.J.: Visual sign information extraction and identification by deformable models for intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems 5(2), 57–68 (2004)
de la Escalera, A., Moreno, L.E., Salichs, M.A., Armingol, J.M.: Road traffic sign detection and classification. IEEE Transactions on Industrial Electronics 44(6), 848–859 (1997)
Fang, C.-Y., Chen, S.-W., Fuh, C.-S.: Road-sign detection and tracking. IEEE Transactions on Vehicular Technology 52(5), 1329–1341 (2003)
Ruta, A., Li, Y., Uxbridge, M., Porikli, F., Watanabe, S., Kage, H., Sumi, K., Amagasaki, J.: A New Approach for In-Vehicle Camera Traffic Sign Detection and Recognition. In: Proc. IAPR Conference on Machine Vision Applications, Japan, (2009)
Timofte, R., Prisacariu, V., Van Gool, L., Reid, I.: Combining TrafficSign Detection with 3D Tracking Towards Better Driver Assistance. Emerging Topics in Computer Vision and Its Applications (2011)
Uchida, T., Hanaizumi, H.: An automated method for understanding road traffic signs in a video scene captured by a mobile camera. In: 2012 IEEE International Conference on Industrial Technology (ICIT), March 19-21, pp. 108–111 (2012)
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 Transactions on Intelligent Transportation Systems 8(2), 264–278 (2007)
Zhang, Z.: Camera calibration with one-dimensional objects. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 161–174. Springer, Heidelberg (2002)
Dröppelmann, S., et al.: Stereo Vision using the OpenCV library (2010)
Choi, S.-I., Zhang, L., Park, S.-Y.: Stereo Vision Based Motion Adjustment of 2D Laser Scan Matching. In: Image and Vision Computing New Zealand, IVCNZ 2011 (November 2011)
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
Choi, CW., Choi, SI., Park, SY. (2013). Efficient Detection and Tracking of Road Signs Based on Vehicle Motion and Stereo Vision. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-02895-8_55
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02894-1
Online ISBN: 978-3-319-02895-8
eBook Packages: Computer ScienceComputer Science (R0)