Shape Matching and Color Segmentation Based Traffic Sign Detection System

• Autorzy: Wali, S. B. , Hannah, M. A. , Abdullah, S. , Hussain, A. , Samad, S. A.

Warianty tytułu

PL

Automatyczny system rozpoznawania znaków drogowych bazujący na dopasowaniu kształtu i segmentacji koloru

• Języki publikacji: EN

Abstrakty

EN

An automatic traffic sign detection system detects traffic signs from within images captured by an imaging sensor, and assists the driver to properly operate the vehicle. The idea presented here is through pixel value detection for hazard traffic signs containing red color background, computing in range regions and finally shape matching to choose the most appropriate traffic sign candidates to be drawn on the screen. The experimental result showed that, by comparing with the similar color segmentation based techniques, the proposed system has a higher accuracy of traffic sign detection rate with a lower computational time.

PL

W artykule opisano system automatycznego rozpoznawania znaków drogowych na podstawie sygnału czujnika obrazu. System rozpoznaje znaki na czerwonym tle, dopasowuje odpowiedni znak i wyświetla go na ekranie.

Słowa kluczowe

PL

rozpoznawanie znaków drogowych; dopasowanie kształtu; segmentacja koloru

EN

traffic sign detection; segmentation; shape matching; detection

• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2015
• Tom: R. 91, nr 1
• Strony: 36-40
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Wali, S. B.

• Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia,

autor

Hannah, M. A.

• Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia

autor

Abdullah, S.

• Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia

autor

Hussain, A.

• Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia

autor

Samad, S. A.

• Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia

Bibliografia

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• [3] Hsien J.C., Chen S.Y. & Liuo Y.S., Traffic Sign Detection and Recognition Using Markov Model, Asian journal of health and Information Sciences, 1 (2006), 85-100
• [4] Escalera A.D.L, Armingol J.M. & Mata M., Traffic Sign Recognition and Analysis for Intelligent Vehicles, Image and Vision Computing, (2003), 21
• [5] Naguwi Y.-Y., Kouzani A.Z., A Study of Automatic Recognition of Traffic Signs, IEEE Conference on Cybernetics and Intelligent Systems, (2006)
• [6] Ying L., Pankanti S., Guan, W., Real time traffic sign detection: an evolutionary study, IEEE International Conference on pattern Recognition, (2010)
• [7] Escalera S., Radeva, P., Fast Grayscale Traffic Sign Model matching and Recognition, Recent Advances in Artificial Intelligence Research and Development, J. Vitria et al. (Eds.) IOS Press, (2004), 69-76
• [8] Fistrek T., Loncaric S., Traffic Sign Detection and Recognition Using Neural Networks and Histogram based Selection of Segmentation Method, 53rd International Symposium ELMAR, (2011)
• [9] Gao X., Shetsova N., Hong K., Batty S., Podladchikova L., Golovan A., Shaposhnikov D., Gusakova V., Vision models based identification of traffic signs, European conference on color in graphics, imaging, and vision, (2002)
• [10] Miura J., Kanda T. and Shirai Y., An active vision system for real-time traffic sign recognition, proc of 2000 IEEE Int. Conf. on Intelligent Transportation Systems, (2000), pp 52–57
• [11] Alfes B., Eschemann G., Ramoser H., Beleznai C., Road sign detection from edge orientation histogram, proc of 2007 IEEE Intelligent Vehicles Symposium, (2007), pp 993–998
• [12] Ruta A., Li Y. & Liu X., Real time traffic sign recognition from video by class-specific discriminative feature, Pattern Recognition, 43 (2010), no 1, 416–430