基于相位对称性和MESR的道路交通标志检测方法研究
Research on Road Sign Detection Method Based on Phase Symmetry and MSER
DOI: 10.12677/CSA.2017.712136, PDF,    国家自然科学基金支持
作者: 浦世亮*：杭州海康威视数字技术股份有限公司，浙江 杭州；王淑丹, 徐向华：杭州电子科技大学计算机学院，浙江 杭州；杨建旭：中国人民银行清算总中心，北京
关键词: 交通标志检测；相位对称性；MSER；GTSDB；Traffic Sign Detection； Phase Symmetry； MSER； GTSD
摘要: 本文提出了一种基于相位对称性的交通标志检测方法，对光照条件变化、尺度变化旋转等情况下都具有较好的适应性。该方法首先根据交通标志的颜色特征，对图像进行红蓝化阈值处理，区分交通标志和环境背景；对红蓝化图像做相位对称性计算处理，增强具有对称性特征的交通标志轮廓亮度；然后，通过形态学滤波和MSER(maximally stable extremal regions)特征检测，提取图像中的交通标志ROI候选区域。通过在德国交通标志数据集上的实验测试German Traffic Sign Detection Benchmark (GTSDB)，该方法对交通标志的检测率到达94%，对于光照条件变化、局部遮挡、旋转尺度变化等复杂情况具有更好的适应性。
Abstract: The high variability of sign appearance in complex road environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we propose a novel approach for the detection of traffic signs, which is well-suitable to different light conditions, change of scale and rotation. The detection process has four stages: image color enhancement, phase symmetry computation, morphological filter and maximally stable extremal regions (MSERs) detection phase. We combine the color information of traffic signs using color enhancement with the symmetry of its shape type, and performing phase symmetry computation to highlight the Region of interest (ROI). Finally, the candidate regions of traffic signs are detected by MSER. The proposed system attains a highly accuracy up to 94% on the German Traffic Sign Detection Benchmark (GTSDB). It has better adaptability to complex conditions such as illumination change, rotation scale change and so on.
文章引用：浦世亮, 王淑丹, 杨建旭, 徐向华. 基于相位对称性和MESR的道路交通标志检测方法研究[J]. 计算机科学与应用, 2017, 7(12): 1206-1220. https://doi.org/10.12677/CSA.2017.712136

参考文献

[1] Handmann, T., Kalinke, C., Tzomakas, M., et al. (1998) An Image Processing System for Driver Assistance. Image and Vision Computing, 18, 367-376. [Google Scholar] [CrossRef] 
[2] Franke, U., Gavrila, D., Gorzig, S., et al. (1998) Autonomous Driving Goes Downtown. IEEE Intelligent Systems & Their Applications, 13, 40-48. [Google Scholar] [CrossRef] 
[3] Madeira, S., Bastos, L., Sousa, A., et al. (2005) Automatic Traffic Signs Inventory Using a Mobile Mapping System. International Conference on Information Systems Gis Planet, Lisboa.
[4] Stallkamp, J., Schlipsing, M., Salmen, J., et al. (2012) Man. vs Computer: Benchmarking Machine Learning Algorithms for Traffic Sign Recognition. Neural Networks the Official Journal of the International Neural Network Society, 32, 323. [Google Scholar] [CrossRef] [PubMed]
[5] Houben, S., Stallkamp, J., Salmen, J., et al. (2013) Detection of Traffic Signs in Real-World Images: The German Traffic Sign Detection Benchmark. International Joint Conference on Neural Networks, 1-8.
[6] Gu, Y., Yendo, T., Tehrani, M., et al. (2011) Traffic Sign Detection in Dual-Focal Active Camera System. Intelligent Vehicles Symposium, 32, 1054-1059. [Google Scholar] [CrossRef] 
[7] Keller, C., Sprunk, C., Bahlmann, C., et al. (2008) Real-Time Recognition of U.S. Speed Signs. Intelligent Vehicles Symposium, 518-523.
[8] Kuo, W.J. and Lin, C.C. (2007) Two-Stage Road Sign Detection and Recognition. IEEE International Conference on Multimedia & Expo, 1427-1430.
[9] Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, P., et al. (2007) Road-Sign Detection and Recognition Based on Support Vector Machines. IEEE Transactions on Intelligent Transportation Systems, 8, 264-278. [Google Scholar] [CrossRef] 
[10] Zaklouta, F. and Stanciulescu, B. (2012) Real-Time Traffic Sign Recognition in Three Stages. Robotics and Autonomous Systems, 62, 16-24. [Google Scholar] [CrossRef] 
[11] Nguwi, Y.Y. and Kouzani, A. (2008) Detection and Classification of Road Signs in Natural Environments. Neural Computing and Applications, 17, 265-289. [Google Scholar] [CrossRef] 
[12] Vázquez-Reina, A., Lafuente-Arroyo, S., et al. (2005) Traffic Sign Shape Classification Based on Correlation Techniques. Wseas International Conference on Signal Processing, 149-154.
[13] Liu, H., Liu, D. and Xin, J. (2002) Real-Time Recognition of Road Traffic Sign in Motion Image Based on Genetic Algorithm. International Conference on Machine Learning & Cybernetics, 1, 83-86. [Google Scholar] [CrossRef] 
[14] Gao, X.L., Podladchikova, D., Shaposhnikov, K., et al. (2006) Recognition of Traffic Signs Based on Their Color and Shape Features Extracted Using Human Vision Models. Journal of Visual Communication & Image Representation, 17, 675-685. [Google Scholar] [CrossRef] 
[15] Creusen, I., Wijnhoven, R., Herbschleb, E., et al. (2010) Color Exploitation in Hog-Based Traffic Sign Detection. IEEE International Conference on Image Processing, 119, 2669-2672. [Google Scholar] [CrossRef] 
[16] Wanitchai, P. and Phiphobmongkol, S. (2008) Traffic Warning Signs Detection and Recognition Based on Fuzzy Logic and Chain Code Analysis, 508-512.
[17] Moutarde, F., Bargeton, A., Herbin, A. and Chanussot, L. (2007) Robust on-Vehicle Real-Time Visual Detection of American and European Speed Limit Signs, with a Modular Traffic Signs Recognition System. Intelligent Vehicles Symposium, 1122-1126.
[18] Loy, G. and Barnes, N. (2004) Fast Shape-Based Road Sign Detection for a Driver Assistance System. IEEE/RSJ International Conference on Intelligent Robots and Systems, 1, 70-75.
[19] Barnes, N. and Loy, G. (2006) Real-Time Regular Polygonal Sign Detection. Field and Service Robotics. Springer-Verlag, New York, 55-66.
[20] Barnes, N., Zelinsky, A. and Fletcher, L. (2008) Real-Time Speed Sign Detection Using the Radial Symmetry Detector. IEEE Transactions on Intelligent Transportation Systems, 9, 322-332. [Google Scholar] [CrossRef] 
[21] Larsson, F. and Felsberg, M. (2011) Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition. Proceedings of the International Conference on Image Analysis and Processing, 238-249.
[22] Ren, F., Huang, J., Jiang, R. and Klette, R. (2009) General Traffic Sign Recognition by Feature Matching. IVCNZ: Image and Vision Computing New Zealand, 409-414.
[23] Xu, S. (2009) Robust Traffic Sign Shape Recognition Using Geometric Matching. IET Intelligent Transport Systems, 3, 10-18. [Google Scholar] [CrossRef] 
[24] Chiang, H.H., Chen, Y.L., Wang, W.Q. and Lee, T.T. (2010) Road Speed Sign Recognition Using Edge-Voting Principle and Learning Vector Quantization Network. Computer Symposium, 246-251.
[25] Qingsong, X., Juan, S. and Tiantian, L. (2010) A Detection and Recognition Method for Prohibition Traffic Signs. International Conference on Image Analysis & Signal Processing, 583-586.
[26] Trivedi, M., Gandhi, T. and McCall, J. (2007) Looking-in and Looking-out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety. IEEE Transactions on Intelligent Transportation Systems, 8, 108-120. [Google Scholar] [CrossRef] 
[27] Gomez-Moreno, H., Maldonado-Bascon, S., Gil-Jimenez, P. and La-fuente-Arroyo, S. (2010) Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition. IEEE Trans. IEEE Transactions on Intelligent Transportation Systems, 11, 917-930. [Google Scholar] [CrossRef] 
[28] Loy, G. and Zelinsky, A. (2003) Fast Radial Symmetry for Detecting Points of Interest. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 959-973. [Google Scholar] [CrossRef] 
[29] Deguchi, D., Shirasuna, M., Doman, K., et al. (2011) Intelligent Traffic Sign Detector: Adaptive Learning Based on Online Gathering of Training Samples. Intelligent Vehicles Symposium, 30, 72-77. [Google Scholar] [CrossRef] 
[30] Kovesi, P. (1997) Symmetry and Asymmetry from Local Phase. Tenth Australian Joint Converence on Artificial Intelligence, 185-190.
[31] Matas, J., Chum, O., Urban, M. and Pajdla, T. (2002) Robust Wide Baseline Stereo from Maximally Stable Extremal regions. Image & Vision Computing, 22, 761-767. [Google Scholar] [CrossRef] 
[32] Greenhalgh, J. and Mirmehdi, M. (2012) Real-Time Detection and Recognition of Road Traffic Signs. IEEE Transactions on Intelligent Transportation Systems, 13, 1498-1506. [Google Scholar] [CrossRef]