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http://dx.doi.org/10.7855/IJHE.2013.15.1.155

The Road Traffic Sign Recognition and Automatic Positioning for Road Facility Management  

Lee, Jun Seok (한국건설기술연구원 도로연구실)
Yun, Duk Geun (한국건설기술연구원 도로연구실)
Publication Information
International Journal of Highway Engineering / v.15, no.1, 2013 , pp. 155-161 More about this Journal
Abstract
PURPOSES: This study is to develop a road traffic sign recognition and automatic positioning for road facility management. METHODS: In this study, we installed the GPS, IMU, DMI, camera, laser sensor on the van and surveyed the car position, fore-sight image, point cloud of traffic signs. To insert automatic position of traffic sign, the automatic traffic sign recognition S/W developed and it can log the traffic sign type and approximate position, this study suggests a methodology to transform the laser point-cloud to the map coordinate system with the 3D axis rotation algorithm. RESULTS: Result show that on a clear day, traffic sign recognition ratio is 92.98%, and on cloudy day recognition ratio is 80.58%. To insert exact traffic sign position. This study examined the point difference with the road surveying results. The result RMSE is 0.227m and average is 1.51m which is the GPS positioning error. Including these error we can insert the traffic sign position within 1.51m CONCLUSIONS: As a result of this study, we can automatically survey the traffic sign type, position data of the traffic sign position error and analysis the road safety, speed limit consistency, which can be used in traffic sign DB.
Keywords
road traffic sign recognition; automatic positioning; road facility management; GIS; GPS;
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  • Reference
1 Le, T., Tran, S., Mita, S., & Nguyen, T. 2010. Real Time Traffic Sign Detection Using Color and Shape-Based Features. In N. Nguyen, M. Le & J. Swiatek (Eds.), Intelligent Information and Database Systems, Vol. 5991, 268-278
2 Shin MinChol. 2006. Traffic Sign Recognition Using Color Information and Neural Networks, Inhwa University master's thesis.
3 E&G Information Technology. 2012. Road Traffic Sign Automatic Recognition S/W Development.
4 Support vector machine. 2012, http://en.wikipedia.org/wiki/Support_vector_machine.
5 Im, Hyunyun, 2006. Traffic Engineering Handbook, Byucksan Engineering, 171.
6 Korea Institute of Construction Technology. 2007, Road Safety Analysis Vehicle(5th), 9-10.
7 de la Escalera, Industrial Electronics. 1997, Road Traffic Sign Detection and Classification, IEEE Transactions on, Vol. 44. 848-859.   DOI   ScienceOn
8 Tam T. Le, et al. 2010, Real Time Traffic Sign Detection Using Color and Shape-Based Features, IJCSIS, vol. 7 No 3.