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Road Space Rationing Using Vehicle's Automated License Plate Recognition: A Comprehensive Survey of Methods
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Author(s): Ata Jahangir Moshayedi (Jiangxi University of Science and Technology, China), Amir Sohail Khan (Jiangxi University of Science and Technology, China), Zeashan Hameed Khan (King Fahd University of Petroleum and Minerals, Saudi Arabia), Arash Sioofy Khoojine (Yibin University, China)and Abolfazl Razi (Clemson University, USA)
Copyright: 2025
Pages: 44
Source title:
Applied Neural Networks in the AI Era: From Theory to Real-World Impact
Source Author(s)/Editor(s): Sarah Benziane (University of Science and Technology in Oran, Algeria)and Fatiha Guerroudji Meddah (University of Science and Technology Mohammed Boudiaf Oran, Algeria)
DOI: 10.4018/979-8-3373-4571-0.ch005
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Abstract
The exponential rise in volumes of motor vehicles all over the world has led to increased pollution and negative impact on climate. Therefore, road space rationing is one strategy to control traffic volume which allows even/odd number plates on alternate days. However, it requires Automated License Plate Recognition (ALPR) systems for real time monitoring. Machine intelligence based ALPR uses multiple technologies to offer robust performance under varying imaging conditions, such as high velocities, low light, harsh weather, camera vibration, and partially obscured images. Furthermore, the radical shift from conventional to deep learning (DL) methods also requires developing tools and data-sets which are capable of handling diverse character sets in multiple languages. This scoping review offers a comparative analysis of various approaches in ALPR systems where AI and deep learning algorithms are found to offer superb performance and high recognition rates of greater than 90% after compiling the contents of around 100 published articles during 1997-2023.
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