Urban Road Traffic Sign Recognition Algorithm Based on Monocular Vision

Xue Cai Yu; De Ren Chen

doi:10.4028/www.scientific.net/KEM.522.711

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HomeKey Engineering MaterialsKey Engineering Materials Vol. 522Urban Road Traffic Sign Recognition Algorithm...

Urban Road Traffic Sign Recognition Algorithm Based on Monocular Vision

Abstract:

Intelligent Transportation System (ITS) is a new industry and development greatly in recent years. The advanced electro-communication and computer technology combines detection, control, active warning and other information means. It provides a great guarantee for improving traffic safety and high efficiency. This paper puts forward the traffic rate-limiting sign and Lane Mark recognition method. In this study, four steps are applied to recognize a traffic and Lane Mark sign in an image. The first step, we transform the color space of image from RGB into HIS. Then the image blended HIS and RGB information is segmented. The third step, the text restrain and eliminate the isolated noise point based on eight-neighborhood. Finally, the traffic speed-limit sign and Lane Mark is recognized with template match. Our experiment results, in the out door, show that the method mentioned in the paper is useful and effective.

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Periodical:

Key Engineering Materials (Volume 522)

Pages:

711-714

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.522.711

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Online since:

August 2012

Authors:

Xue Cai Yu, De Ren Chen

Keywords:

Histogram Analysis, Machine Vision, Optimal Threshold, Transportation Information, Transportation Safety

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References

[1] T.H. Yu : Study on Vision-based Lane Departure Warming System (Ph. D, Jilin University, China 2006). pp.10-42.

Google Scholar

[2] X.N. Yang, J.M. Duan, D.Z. Gao and B.G. Zheng: Computer Measurement & Control, Vol. 18 (2010) No. 2, pp.292-298.

Google Scholar

[3] Yong-Jian Zheng. Ritter, W. Janssen, R: An adaptive system for traffic sign -Intelligent Vehicles(1994) . pp.165-170.

DOI: 10.1109/ivs.1994.639496

Google Scholar

[4] Zhenping Xie, Shitong Wang, Dian You Zhang, F.L. Chung and Hanbin: Image Segmentation Using the Enhanced Possibilistic Clustering Method(Information Technology Journal, 2009). pp.541-546.

DOI: 10.3923/itj.2007.541.546

Google Scholar

[5] M. de Saint Blancard, 1992. Road sign recognition: A study of vision-based decision making for road environment recognition(Springer-Verlag, Germany). pp.186-190.

DOI: 10.1007/978-1-4612-2778-6_7

Google Scholar

[6] Madeira, S., et al: Automatic traffic signs inventory using a mobile mapping system. (2005). pp.158-160.

Google Scholar