Please use this identifier to cite or link to this item: https://scholar.ptuk.edu.ps/handle/123456789/329
Title: Logistic Regression Based Model for Improving the Accuracy and Time Complexity of ROI’s Extraction in Real Time Traffic Signs Recognition System
Authors: Daraghmi, Yousef-Awwad
Daraghmi, Eman
Qararyah, Fareed
Keywords: Logistic regression
Accuracy
Issue Date: 1-Dec-2018
Publisher: JOURNAL OF COMPUTER SCIENCE RESEARCH
Citation: -
Series/Report no.: 1;1
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.
Description: -
URI: https://scholar.ptuk.edu.ps/handle/123456789/329
ISSN: 2630-5151
Appears in Collections:Engineering and Technology Faculty

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