Abstract
A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
OVERETT G, PETERSSON L, ANDERSSON L, PETTERSSON N. Boosting a heterogeneous pool of fast hog features for pedestrian and sign detection [C]// 2009 IEEE Intelligent Vehicles Symposium, Xi’an, 2009: 584–590.
BELAROUSSI R, TAREL J. Angle vertex and bisector geometric model for triangular road sign detection [C]// 2009 Workshop on Applications of Computer Vision, Snowbird, UT, 2009: 1–7.
UETA T, SUMI Y, YABUKI N, MATSUMAE S. A study on contour line and internal area extraction method by using the self-organization map [C]// International Symposium on Intelligent Signal Processing and Communications, Tottori, Japan, 2006: 685–688.
BAHLMANN C, ZHU Y, RAMESH V, PELLKOFER M, KOEHLER T. A system for traffic sign detection, tracking, and recognition using color, shape, and motion information[C]// IEEE 2005 Proceeding on Intelligent Vehicles Symposium, Las Vegas, 2005, 255–260.
RUTA A, LI Yong-Ming, LIU Xiao-Hui. Towards real-time traffic sign recognition by class-specific discriminative features [C]// Proceeding of the 18th British Machine Vision Conference, Warwick, 2007: 399–408.
KANTAWONG S. Road Traffic Signs Detection and Classification for Blind Man Navigation System [C]// Proceeding of international Conference on Control, Automation and Systems (ICCAS), Seoul, 2007: 847–852.
NGUWI Y, KOUZANI A. Automatic road sign recognition using neural networks [C]// International Joint Conference on Neural Networks, Vancouver, 2006: 3955–3962.
REN F X, HUANG J, JIANG R, KLETTE R. General traffic sign recognition by feature matching [C]// Proceedings of 24th International Conference of Image and Vision Computing. New Zealand: IEEE, 2009: 409–414.
SHADEED W, ABU-AL-NADI D, MISMAR M. Road traffic sign detection in color images [C]// Proceedings of the 2003 10th IEEE International Conference on Electronics, Circuits and Systems, Sharjah, 2003: 890–893.
GAO X W, PODLADCHIKOVA L, SHAPOSHNIKOV D, HONG K, SHEVTSOVA N. Recognition of traffic signs based on their colour and shape features extracted using human vision models [J]. Journal of Visual Communication and Image Representation, 2006, 17(4): 675–685.
ALAN K, HOLGER J, SAMAN H. Gabor wavelet similarity maps for optimising hierarchical road sign classifiers [J]. Pattern Recognition Letters, 2007, 28(2): 260–267.
HOSSAIN M S, HASAN M M, ALI M A, KABIR M H, ALI A B M S. Automatic detection and recognition of traffic signs [C]// Proceedings of 2010 IEEE Conference on Robotics Automation and Mechatronics (RAM), Singapore, 2010: 286–291
RUTA A, LI Yong-ming, LIU Xiao-hui. Real-time traffic sign recognition from video by class-specific discriminative features [J]. Pattern Recognition, 2010, 43(1): 416–430
CYGANEK B. Road Signs Recognition by the Scale-Space Template Matching in the Log-Polar Domain [J]. Pattern Recognition and Image Analysis, 2007, 4447: 330–337.
MALDONADO-BASCÓN S, LAFUENTE-ARROYO S, GIL-JIMÉNEZ P, GÓMEZ-MORENO H, LÓPEZ-FERRERAS F. Road-sign detection and recognition based on support vector machines [J]. IEEE Transactions on Intelligent Transportation System, 2007, 8(2): 264–278.
MEUTER M, MULLER-SCHNEIDERS S, NUNNY C, HOLDY S, GOERMERY S, KUMMERTY A. Decision fusion and reasoning for traffic sign recognition [C]// Proceedings of 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), Funchal, 2010: 324–329.
LIM K H, SENG K P, ANG L M. Intra color-shape classification for traffic sign recognition [C]// Proceedings of 2010 International Conference of Computer Symposium, Tainan, 2010, 642–647.
DENG Xiao, WANG Dong-hui, CHENG Li-li, KONG Shu. Traffic Sign Recognition Using Dictionary Learning Method [C]// Proceedings of 2010 Second WRI Global Congress on Intelligent Systems (GCIS), Wuhan, 2010, 372–375.
SELESNICK I W, BARANIUK R G, KINGSBURY N C. The dual-tree complex wavelet transform [J]. Signal Processing Magazine, IEEE. 2005, 22(6): 123–151.
HYVARINEN A, OJA E. Independent component analysis: algorithms and applications [J]. Neural networks. 2000, 13(4/5): 411–430.
REN Xiao-ping, CAI Zi-xing, CHEN Bai-fan, YU Ling-li. Anomaly detection method based on kinematics model and nonholonomic constraint of vehicle [J]. Journal of Central South University of Technology, 2011, 18(4): 1128–1132.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: Projects(90820302, 60805027) supported by the National Natural Science Foundation of China; Project(200805330005) supported by Research Fund for Doctoral Program of Higher Education, China; Project(2009FJ4030) supported by Academician Foundation of Hunan Province, China
Rights and permissions
About this article
Cite this article
Cai, Zx., Gu, Mq. Traffic sign recognition algorithm based on shape signature and dual-tree complex wavelet transform. J. Cent. South Univ. 20, 433–439 (2013). https://doi.org/10.1007/s11771-013-1504-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11771-013-1504-0