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Circular Traffic Signs Recognition Using The Number of Peaks Algorithm
Khaled M. Almustafa
Pages - 514 - 522     |    Revised - 01-12-2014     |    Published - 31-12-2014
Volume - 8   Issue - 6    |    Publication Date - November / December 2014  Table of Contents
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KEYWORDS
Traffic Signs Recognition, Pattern Recognition, Image Processing, Autonomous Cars.
ABSTRACT
Smart cars nowadays include embedded computers to guide the driver in his trip. An important application that should be added to any car is the detection and recognition of traffic signs. In this paper, we focus on the recognition of a wide set of circular traffic signs using the Number of Peaks Algorithm [1]. After detecting a traffic sign, the algorithm draws three horizontal lines and three vertical lines across the image. The number of peaks (crossing from a black pixel to a white pixel) is calculated for each of the six lines as the image is scanned from right to left (for horizontal lines) or top to bottom (for vertical lines). The resulting numbers of peaks are used by the decision-tree-like search algorithm to distinguish between 51 circular road signs with a mean detection time of 8 milliseconds, 100% detection rate and in a fairly noisy environment.
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1 L. Hamandi, K. AlMustafa, R. Zantout, H. Obeid, Recognition of Triangular Traffic Signs Using the Number of Peaks Algorithm, accepted for publication in The 2nd International Conference On Advances In Computational Tools For Engineering Applications, Dec. 12- 15, 2012, Beirut, Lebanon.
2 K. Baba, Y. Hirai, Real-time recognition of traffic signs using opponent color, Proceedings of the 14th World Congress on Intelligent Transport Systems (ITS) 2007, pp. 5127-5138.
3 M. H. Alsibai, Y. Hirai , Real-Time Recognition of Blue Traffic Signs Designating Directions, International Journal of Intelligent Transportation Systems Research, May 2010, Volume 8, Issue 2, pp 96-105.
4 G. Piccioli, E. De Micheli, P. Parodi, M. Campani, A robust method for road sign detection and recognition, Image and Vision Computing 14 (3) (1996) pp. 209–223.
5 A. de la Escalera, L.E. Moreno, M.A. Salichs, J.M. Armingol, Road traffic sign detection and classification, IEEE Transactions on Industrial Electronics 44 (6) (1997) pp. 848–859.
6 P. Paclík, J. Novovicova, P. Pudil, P. Somol, Road signs classification using the Laplace kernel classifier, Pattern Recognition Letters 21 (13–14) (2000) pp. 1165–1173.
7 A. Ruta, Yongmin Li,Xiaohui Liu./ Real-time traffic sign recognition from video by class- specific discriminative features, Pattern Recognition 43(2010) pp. 416—430.
8 A. de la Escalera, J.M. Armingol, J.M. Pastor, F.J. Rodríguez, Visual sign information extraction and identification by deformable models for intelligent vehicles, IEEE Transactions on Intelligent Transportation Systems 5 (2) (2004) pp. 57–68.
9 P. Paclík, J. Novovicová, R.P.W. Duin, Building road-sign classifiers using a trainable similarity measure, IEEE Transactions on Intelligent Transportation Systems 7 (3) (2006) pp. 309–321.
10 X.W. Gao, L. Podladchikova, D. Shaposhnikov, K. Hong, N. Shevtsova, Recognition of traffic signs based on their colour and shape features extracted using human vision models, Journal of Visual Communication and Image Representation 17 (4) (2006) pp. 675–685.
11 Escalera et. al. Traffic sign recognition system with ß-correction Machine Vision and Applications (2010) 21, pp. 99–111.
12 Y. Aoyagi, T. Asakura, A study on traffic sign recognition in scene image using genetic algorithms and neural networks, Proceedings of the 1996 IEEE IECON 22nd International Conference on Industrial Electronics, Control, and Instrumentation, vol. 3, 1996, pp. 1838– 1843.
13 C. Bahlmann, Y. Zhu, V. Ramesh, M. Pellkofer, T. Koehler, A system for traffic sign detection, tracking and recognition using color, shape, and motion information, in: Proceedings of the IEEE Intelligent Vehicles Symposium, 2005, pp. 255–260.
14 P. Douville, Real-time classification of traffic signs, Real-Time Imaging 6 (3) (2000) pp. 185– 193.
15 C.-Y. Fang, S.-W. Chen, C.-S. Fuh, Roadsign detection and tracking, IEEE Transactions on Vehicular Technology 52 (5) (2003) pp. 1329–1341.
16 H. Fleyeh, Color detection and segmentation for road and traffic signs, in: Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, Singapore, vol. 2, 2004, pp. 809– 814.
17 M.A. Garcia-Garrido, M.A. Sotelo, E. Martin-Gorostiza, Fast traffic sign detection and recognition under changing lighting conditions, in: Proceedings of the IEEE International Conference on Intelligent Transportation Systems, 2006, pp. 811–816.
18 Hatzidimos, Automatic Traffic Sign Recognition in Digital Images, Proceedings of the International Conference on Theory and Applications of Mathematics and Informatics - ICTAMI 2004, Thessaloniki, Greece, pp. 174 – 184.
19 J. Miura, T. Kanda, Y. Shirai, An active vision system for real-time traffic sign recognition, in: Proceedings of the IEEE Conference on Intelligent Transportation Systems, Darborn, MI, USA, 2000, pp. 52–57.
20 A. Ruta, Y. Li, X. Liu, Towards real-time traffic sign recognition by class-specific discriminative features, in: Proceedings of the 18th British Machine Vision Conference, Coventry, United Kingdom, vol. 1, 2007, pp. 399–408.
21 A. Ruta, Y. Li, X. Liu, Traffic sign recognition using discriminative local features, in: Proceedings of the 7th International Symposium on Intelligent Data Analysis, Ljubljana, Slovenia, 2007, pp. 355–366.
22 K. AlMustafa, R. Zantout, H. Obeid, “Peak Position, Recognizing Characters in Saudi License Plates”, 2011 IEEE GCC Conference and Exhibition for Sustainable Ubiquitous Technology, Dubai, United Arab Emirates, February 19-22, 2011, pp. 186-189.
Dr. Khaled M. Almustafa
Prince Sultan University. P.O.Box No. 66833 Riyadh 11586, K.S.A - Saudi Arabia
khaledalmustafa@gmail.com