A Method of Detecting and Recognizing Speed-limit Signs

This paper proposes a novel speed-limit sign detection and recognition method by using only gray-level information. This method has a real-time processing ability to remind drivers about the speed limit when they are driving on roads, and it contains four main processing modules: speed-limit sign detection, speed-limit sign segmentation, speed-limit sign recognition and system integration. For detecting speed limit signs, both Adaboost and Circular Hough Transform (CHT) are used. For recognizing speed-limit signs, Support Vector Machine is applied and a high recognition performance up to 97.02% is achieved in our experiments. By integrating the four processing modules efficiently, a high efficient speed-limit sign detection and recognition system has been developed.

[1]  Jun-Wei Hsieh,et al.  Boosted road sign detection and recognition , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[2]  Jun-Wei Hsieh,et al.  Road sign detection using eigen colour , 2008 .

[3]  A. Techmer,et al.  Real time motion analysis for monitoring the rear and lateral road , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[4]  Shuangdong Zhu,et al.  Traffic Sign Recognition based on Color Standardization , 2006, 2006 IEEE International Conference on Information Acquisition.

[5]  Yingfeng Cai,et al.  A Lane Departure Warning System Based on Machine Vision , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[6]  C. Beleznai,et al.  Road Sign Detection from Edge Orientation Histograms , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[7]  K. Jo,et al.  Automatic Detection and Recognition of Traffic Signs using Geometric Structure Analysis , 2006, 2006 SICE-ICASE International Joint Conference.

[8]  Yok-Yen Nguwi,et al.  Automatic Road Sign Recognition Using Neural Networks , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[9]  Jordi Vitrià,et al.  Fast Traffic Sign Detection on greyscale images , 2004 .

[10]  Francisco López-Ferreras,et al.  Road-Sign Detection and Recognition Based on Support Vector Machines , 2007, IEEE Transactions on Intelligent Transportation Systems.