[1] Zhang Ning, He Tiejun, Gao Zhaohui, Huang Wei, et al. Traffic light detection and recognition in intersectionsbased on intelligent vehicle [J]. Journal of Southeast University (English Edition), 2008, 24 (4): 517-521. [doi:10.3969/j.issn.1003-7985.2008.04.024]

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Traffic light detection and recognition in intersectionsbased on intelligent vehicle()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

24

Issue:

2008 4

Page:

517-521

Research Field:

Traffic and Transportation Engineering

Publishing date:

2008-12-30

Info

Title:

Traffic light detection and recognition in intersectionsbased on intelligent vehicle

Author(s):

Zhang Ning;  He Tiejun;  Gao Zhaohui;  Huang Wei

ITS Research Center, Southeast University, Nanjing 210096, China

Keywords:

intelligent vehicle;  stabling siding detection;  traffic lights detection;  self-associative memory;  light-emitting diode(LED)characters recognition

PACS:

U491

DOI:

10.3969/j.issn.1003-7985.2008.04.024

Abstract:

To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights.First, the stabling siding at intersections is detected by applying Hough transformation.Then, the colors of traffic lights are detected with color space transformation.Finally, self-associative memory is used to recognize the countdown characters of the traffic lights.Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively.The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.

References:

[1] Broggi A, Cattani S.An agent based evolutionary approach to path detection for off-road vehicle guidance[J].Pattern Recognition Letters, 2006, 27(11):1164-1173.
[2] Zuo Xiaoqing, Li Qingquan, Xie Zhiying.Lane-based road data model[J].Journal of Chang’an University:Natural Science Edition, 2004, 24(2):73-76.(in Chinese)
[3] Wang Rongben, Zhao Yibing, Li Linhui, et al.Approach review of obstacle detection for intelligent vehicle[J].Journal of Highway and Transportation Research and Development, 2007, 24(11):109-113.(in Chinese)
[4] Bayouth M, Nourbakhsh I, Thorpe C.A hybrid human-computer autonomous vehicle architecture[C]//Third ECPD International Conference on Advanced Robotics, Intelligent Automation and Active Systems.Bremen:International Program Committee, 1997:1-7.
[5] Bellutta P, Manduchi R, Matthies L, et al.Terrain perception for DEMOⅢ[C]//Proceedings of the IEEE Intelligent Vehicles Symposium. Bergh:IEEE, 2000:326-331.
[6] Broggi A, Bertozzi M, Fascioli A, et al.Automatic vehicle guidance:the experience of the ARGO autonomous vehicle[R].Singapore:World Scientific, 1999.
[7] Xu Youchun, Wang Rongben, Li Keqiang, et al.A linear model based road identification algorithm[J].Journal of Image and Graphics, 2004, 9(7):858-864.(in Chinese)
[8] Li Liang.Study on monitor techniques of vehicles peccancy based on moving image recognition[D].Dalian:School of Electronic and Information Engineering of Dalian University of Technology, 2005.
[9] Hagan M T, Demuth H B, Beale M.Neural network design[M].Beijing:China Machine Press, 2005:99-111.
[10] Sarangi S, Rath N P.Performance analysis of fuzzy-based canny edge detector[C]//Conference on Computational Intelligence and Multimedia Applications.Sivakasi:IEEE, 2007, 3:272-276.
[11] Dandawate Y H, Joshi M A, Chitre A V.Quality analysis of color images compressed with enhanced vector quantizer designed using HSI color space[C]//Conference on Computational Intelligence and Multimedia Applications.Sivakasi:IEEE, 2007, 3:138-142.
[12] Yung N H C, Lai A H S.An effective video analysis method for detecting red light runners[J].IEEE Transactions on Vehicular Technology, 2001, 50(4):1074-1084.
[13] Shen H, Roysam B, Stewart C V, et al.Optimal scheduling of tracing computations for real-time vascular landmark extraction from retinal fundus images[J].IEEE Transactions on Information Technology in Biomedicine, 2001, 5(1):77-91.

Memo

Memo:

Biography: Zhang Ning(1972—), male, doctor, associate professor, ningzhang1972@yahoo.com.cn.
Foundation item: The Cultivation Fund of the Key Scientific and Technical Innovation Project of Higher Education of Ministry of Education(No.705020).
Citation: Zhang Ning, He Tiejun, Gao Zhaohui, et al.Traffic light detection and recognition in intersections based on intelligent vehicle[J].Journal of Southeast University(English Edition), 2008, 24(4):517-521.

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