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

Authors

  • 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

DOI:

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

Abstract

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.

Keywords:

Logistic regression; Traffic sign recognition systems

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How to Cite

Qararyah, F., Daraghmi, Y.-A., & Daraghmi, E. Y. (2019). Logistic Regression Based Model for Improving the Accuracy and Time Complexity of ROI’s Extraction in Real Time Traffic Signs Recognition System. Journal of Computer Science Research, 1(1), 10–15. https://doi.org/10.30564/jcsr.v1i1.442

Issue

Vol. 1 , Iss. 1 (April 2019)

Article Type

Review

License

Copyright © 2019 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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