Logistic Regression Based Model for Improving the Accuracy and Time Complexity of ROI's Extraction in Real Time Traffic Signs Recognition System

Fareed Qararyah (Department of Computer Engineering, Koç University, Turkey)
Yousef-Awwad Daraghmi (Computer Systems Engineering Department , College of engineering and Technology, Palestine Technical University-Kaddorie, Palestine)
Eman Yasser Daraghmi (Applied Computing Department , College of Applied Science, Palestine Technical University-Kaddorie, Palestine)


Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system. Traffic sign recognition systems consist of an initial detection phase where images and colors are segmented and fed to the recognition phase. The most challenging process in such systems in terms of time consumption is the detection phase. The tradeoff in previous studies, which proposed different methods for detecting traffic signs, is between accuracy and computation time. Therefore, this paper presents a novel accurate and time-efficient color segmentation approach based on logistic regression. We used RGB color space as the domain to extract the features of our hypothesis; this has boosted the speed of our approach since no color conversion is needed. Our trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting conditions. The results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method.


Logistic regression;Traffic sign recognition systems

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[1] C.-Y. Fang, S.-W. Chen, and C.-S. Fuh, "Road-Sign Detection and Tracking," IEEE Trans. Veh. Technol., 2003, 52(5).

[2] H. Fleyeh, "Color detection and segmentation for road and traffic signs," in IEEE Conference on Cybernetics and Intelligent Systems, 2004, 2, 809–814.

[3] H. Luo, Y. Yang, B. Tong, F. Wu, and B. Fan, "Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network," IEEE Trans. Intell. Transp. Syst., 2018, 19(4), 1100–1111.

[4] H. Fleyeh, "Road and Traffic Sign Color Detection and Segmentation - A Fuzzy Approach," in Conference on Machine VIsion Applications, 2005, 3–27.

[5] E. De Micheli, R. Prevete, G. Piccioli, and M. Campani, "Color cues for traffic scene analysis," in Proceedings of the Intelligent Vehicles' 95. Symposium, 466–471.

[6] N. Beg, M. Agrawal, R. Pasari, A. Singh, and K. H. Wanjale, "Traffic Sign Recognition System," Int. J. Res. Advent Technol., 2016.

[7] M. S. Hossain, M. M. Hasan, M. Ameer Ali, M. H. Kabir, and a B. M. Shawkat Ali, "Automatic detection and recognition of traffic signs," 2010 IEEE Conf. Robot. Autom. Mechatronics, 2010, 286–291.

[8] A. Broggi, P. Cerri, P. Medici, P. P. Porta, and G. Ghisio, "Real Time Road Signs Recognition," 2007 IEEE Intell. Veh. Symp., no. section III[1] A. Broggi, P. Cerri, P. Medici, P. P. Porta, and G. Ghisio, "Real Time Road Signs Recognition," 2007 IEEE Intell. Veh. Symp., section III, 2007, 981–986,981–986.

[9] A. Shustanov and P. Yakimov, "CNN Design for Real-Time Traffic Sign Recognition," Procedia Eng., 2017, 201, 718–725.

[10] R. Timofte, K. Zimmermann, and L. Van Gool, "Multiview traffic sign detection, recognition, and 3D localisation," Mach. Vis. Appl.,2014, 25(3), 633–647.

[11] M. Benallal and J. Meunier, "Real-time color segmentation of road signs," in CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436), 2003, 3, 1823–1826.

[12] Y. Yuan, Z. Xiong, and Q. Wang, "An Incremental Framework for Video-Based Traffic Sign Detection, Tracking, and Recognition," IEEE Trans. Intell. Transp. Syst., 2017,18(7), 1918–1929.

[13] S. Salti, A. Petrelli, F. Tombari, N. Fioraio, and L. Di Stefano, "A traffic sign detection pipeline based on interest region extraction," Proc. Int. Jt. Conf. Neural Networks, 2013.

[14] S. Caballé and J. Conesa, Software Data Engineering for Network eLearning Environments, 2018, 11. Cham: Springer International Publishing.

[15] P. Arnoul, M. Viala, J. P. Guerin, and M. Mergy, "Traffic signs localisation for highways inventory from a video camera on board a moving collection van," in Proceedings of Conference on Intelligent Vehicles, pp. 141–146.

[16] L. Priese, R. Lakmann, and V. Rehrmann, "Ideogram identification in a realtime traffic sign recognition system," in Proceedings of the Intelligent Vehicles '95. Symposium, pp. 310–314.

[17] A. Mavrinac, J. Wu, X. Chen, and K. Tepe, "Competitive learning techniques for color image segmentation," Proc. Mach. Learn. Comput. Vis., 2007, 88, (590), 33–37.

[18] S. B. Wali, M. A. Hannan, S. Abdullah, A. Hussain, and S. A. Samad, "Shape Matching and Color Segmentation Based Traffic Sign Detection System," Prz. Elektrotechniczny, 2015,91(1), 36–40.

DOI: https://doi.org/10.30564/jcsr.v1i1.442


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