Browse > Article

Traffic Sign Detection Using The HSI Eigen-color model and Invariant Moments  

Kim, Jong-Bae (Dept. of Computer Eng., Seoul Digital University)
Park, Jung-Ho (Dept. of Computer Eng., Seoul Digital University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.47, no.1, 2010 , pp. 41-51 More about this Journal
Abstract
In the research for driver assistance systems, traffic sign information to the driver must be a very important information. Therefore, the detection system of traffic signs located on the road should be able to handel real-time. To detect the traffic signs, color and shape of traffic signs is to use the information after images obtained using the CCD camera. In the road environment, however, using color information to detect traffic sings will cause many problems due to changes of weather and environmental factors. In this paper, to solve it, the candidate traffic sign regions are detected from road images obtained in a variety of the illumination changes using the HSI eign-color model. And then, using the invariant moment-based SVM classifier to detect traffic signs are proposed. Experimental results show that, traffic sign detection rate is 91%, and the processing time per frame is 0.38sec. Proposed method is useful for real-time intelligent traffic guidance systems can be applied.
Keywords
eigen-color model; invariant moments; driver assistance system; svm;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. P. Miguel, R. A. Alastair, "Using self-organizing maps in the detection and recognition of road signs", Image and vision computing, vol.27, no.6, pp.673-683, 2009.   DOI   ScienceOn
2 Y. I. Ohat, T. Kanada, T. Sakai, "Color information for region segmentation", computer graphics and image processing, vol.13, pp.222-241, 1980.   DOI   ScienceOn
3 S. Varan, S. Singh, R. S. Kunte, R. D> S. Samuel, B. Philip, "A road traffic signal recognition system based on template matching employing tree classifier", IEEE conf. comp. intelligence and multimedia applications, vol. 3, pp.360-365, 2007.
4 R. C. Gonzalez, and R. E. Woods, Digital image processing, 2th, Prentice-Hall, 2002.
5 J. B. Kim "Indoor positioning using the WLAN-based Wavelet and Neural Network", Journal of the IEEK, vol.45CI, no.5, pp. 38-47, 2008.
6 S. Xu, "Robust traffic sign shape recognition using geometric matching", IET ITS, vol.3, no.1, pp.10-18, 2009.
7 M. A. G. Garrido, M. A. Sotelo, E. M. Gorostiza, "Fast traffic sign detection and recognition under changing lighting conditions", IEEE conf. intelligent transportation systems, pp.811-816, 2006.
8 R. Andrzej, L. Youngmin, L. Xiaohui, "Real-time traffic sign recognition from video by class-specific discriminative features", PR, vol.43, no.1, pp.416-430, 2010.
9 L. W. Tsai, J. W. Chuang, C. H. Tseng, Y. J. Fan, K. C. Lee., "Road sign detection using eigen colour", IET computer vision, vol.2, no.3, pp.164-177, 2008.   DOI   ScienceOn
10 W. Ritter, F. Stein, R. Janssen, "Traffic sign recognition using color information", Mathematical and computer modelling, vol. 22, no. 4-7, pp.149-157, 1995.   DOI
11 P. G. Jimenez, S. M. Bascon, H. G. Moreno, S. :. Arroyo, F. L. Ferreras, "Traffic sign shape classification and localization based on the normalized FFT of the signature of blobs and 2D homographies", Signal processing, vol.88, pp.2943-2955, 2008.   DOI   ScienceOn
12 J. M. Armingol, A de la Escalera, J. M. Collado, and J. Rodriguez, "Intelligent vehicle based on visual information", Robotics and autonomous systems, vol.55, pp.904-916, 2007.   DOI   ScienceOn
13 W. G. Shadeed, D. I. A, A. Nadi, M. J. Mismar, "Road traffic sign detection in color images", IEEE conf. electronics circuits and systems, vol.2, pp.14-17. 2003.
14 E. Ulay, G. B. Akar, M. M. Bulut, "Color and shape based traffic sign detection", IEEE conf. SPCA, pp.9-11, 2009.
15 J. B. Kim, "Real-time moving object recognition and tracking using the wavelet-based neural network and invariant moments", Journal of the IEEK, vol.45SP, no.4, pp. 304-315, 2008.
16 Y. Xie, L. F. Liu, C. H. Li, Y. Y. Qu, "Unifying visual saliency with HOG feature learning for traffic sing detection", IEEE conf. intelligent vehicles symposium, pp.24-29, 2009.
17 L. Fletcher, N. Apostoloff, L. Petersson, A. Zelinsky, "Vision in and out of vehicles", IEEE Intelligent systems, vol.18, no.3, pp.12-17, 2003.   DOI   ScienceOn
18 P. Medici, C. Caraffi, E. Cardarelli, P. P. Porta, G. Ghisio, "Real time road sings classification", IEEE conf. vehicular electronics and safety, pp. 253-258, 2008.
19 A. de la Escalera, J. M. Armingol, M. Mata, "Traffic sign recognition and analysis for intelligent vehicles", Image and vision computing, vol.21, no.3, pp.247-258, 2003.   DOI   ScienceOn