License plate recognition for campus auto-gate system
Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers� interest to provide more reliable LPR systems. Several LPR techniques have b...
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Institute of Advanced Engineering and Science
2023
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| _version_ | 1833411762204966912 |
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| author | Yaacob N.L. Alkahtani A.A. Noman F.M. Zuhdi A.W.M. Habeeb D. |
| author2 | 57219416293 |
| author_facet | 57219416293 Yaacob N.L. Alkahtani A.A. Noman F.M. Zuhdi A.W.M. Habeeb D. |
| author_sort | Yaacob N.L. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers� interest to provide more reliable LPR systems. Several LPR techniques have been reported in the literature in different intelligent transportation applications and surveillance systems, and yet a ropust LPR system remains a challenging research task. Because the performance of current techniques is subject to several factors and local conditions, this paper aims to explore the use of LPR in a specific application; i.e. Automatic plate recognition to monitor the entry and exit of vehicles at the university campus gates. Implementing an auto-gate system is an important application for a smooth control of flowing traffic especially during peak hours. We propose an automated system with the ability to capture, verify and recognize the license plates using image processing-based techniques. The system is aimed to work alongside existing access cards and other gate remote controls. Experimental evaluation of the system reveals a detection accuracy of 91.58%, a successful plate number segmentation rate of 91% and 80% accuracy of plate recognition. � 2021 Institute of Advanced Engineering and Science. All rights reserved. |
| format | Article |
| id | my.uniten.dspace-26612 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | my.uniten.dspace-266122023-05-29T17:12:47Z License plate recognition for campus auto-gate system Yaacob N.L. Alkahtani A.A. Noman F.M. Zuhdi A.W.M. Habeeb D. 57219416293 55646765500 55327881300 56589966300 57219414936 Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers� interest to provide more reliable LPR systems. Several LPR techniques have been reported in the literature in different intelligent transportation applications and surveillance systems, and yet a ropust LPR system remains a challenging research task. Because the performance of current techniques is subject to several factors and local conditions, this paper aims to explore the use of LPR in a specific application; i.e. Automatic plate recognition to monitor the entry and exit of vehicles at the university campus gates. Implementing an auto-gate system is an important application for a smooth control of flowing traffic especially during peak hours. We propose an automated system with the ability to capture, verify and recognize the license plates using image processing-based techniques. The system is aimed to work alongside existing access cards and other gate remote controls. Experimental evaluation of the system reveals a detection accuracy of 91.58%, a successful plate number segmentation rate of 91% and 80% accuracy of plate recognition. � 2021 Institute of Advanced Engineering and Science. All rights reserved. Final 2023-05-29T09:12:46Z 2023-05-29T09:12:46Z 2021 Article 10.11591/ijeecs.v21.i1.pp128-136 2-s2.0-85092669432 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092669432&doi=10.11591%2fijeecs.v21.i1.pp128-136&partnerID=40&md5=89945bcfe2504f3e4cf6632fdbac899f https://irepository.uniten.edu.my/handle/123456789/26612 21 1 128 136 All Open Access, Gold, Green Institute of Advanced Engineering and Science Scopus |
| spellingShingle | Yaacob N.L. Alkahtani A.A. Noman F.M. Zuhdi A.W.M. Habeeb D. License plate recognition for campus auto-gate system |
| title | License plate recognition for campus auto-gate system |
| title_full | License plate recognition for campus auto-gate system |
| title_fullStr | License plate recognition for campus auto-gate system |
| title_full_unstemmed | License plate recognition for campus auto-gate system |
| title_short | License plate recognition for campus auto-gate system |
| title_sort | license plate recognition for campus auto-gate system |
| url_provider | http://dspace.uniten.edu.my/ |