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Proceedings Paper

Traffic sign recognition by color segmentation and neural network
Author(s): Thongchai Surinwarangkoon; Supot Nitsuwat; Elvin J. Moore
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Paper Abstract

An algorithm is proposed for traffic sign detection and identification based on color filtering, color segmentation and neural networks. Traffic signs in Thailand are classified by color into four types: namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber). A color filtering method is first used to detect traffic signs and classify them by type. Then color segmentation methods adapted for each color type are used to extract inner features, e.g., arrows, bars etc. Finally, neural networks trained to recognize signs in each color type are used to identify any given traffic sign. Experiments show that the algorithm can improve the accuracy of traffic sign detection and recognition for the traffic signs used in Thailand.

Paper Details

Date Published: 13 January 2012
PDF: 6 pages
Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83501K (13 January 2012); doi: 10.1117/12.920243
Show Author Affiliations
Thongchai Surinwarangkoon, King Mongkut's Univ. of Technology North Bangkok (Thailand)
Supot Nitsuwat, King Mongkut's Univ. of Technology North Bangkok (Thailand)
Elvin J. Moore, King Mongkut's Univ. of Technology North Bangkok (Thailand)


Published in SPIE Proceedings Vol. 8350:
Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies
Safaa S. Mahmoud; Zhu Zeng; Yuting Li, Editor(s)

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