Electronic Number Plate Generation for Performance Evaluation

Soodamani Ramalingam; Bill Martin; Michael Rhead,; Robert Gurney

doi:10.1109/ICCST52959.2022.9896515

Electronic Number Plate Generation for Performance Evaluation

Soodamani Ramalingam, Bill Martin, Michael Rhead,, Robert Gurney

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

The authors have been involved in real world analysis of Automatic Number Plate Recognition (ANPR) data and systems particularly for law enforcement applications. As a result of such work with Law Enforcement Agencies, contributions have been made to the revision of the British Standards for ANPR. This led to the research team developing performance evaluation measures from an end-to-end system perspective. One such measure was the generation of synthetic image datasets suitable for ANPR performance evaluation. The prime requirement for any ANPR system is data accuracy. This paper reports the initial work and progress made using defined synthetic images to test and assess ANPR engines using a structured methodology.

Original language	English
Title of host publication	IEEE International Carnahan Conference on Security Technology (ICCST)
Subtitle of host publication	IEEE ICCST2022
Place of Publication	Valeč u Hrotovic, Czech Republic
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	6
ISBN (Electronic)	978-1-6654-9363-5
ISBN (Print)	978-1-6654-9364-2
DOIs	https://doi.org/10.1109/ICCST52959.2022.9896515
Publication status	Published - 26 Sept 2022
Event	The 55th Annual International Carnahan Conference on Security Technology (ICCST 2022) - Valez, Czech Republic Duration: 7 Sept 2022 → 9 Sept 2022 https://site.ieee.org/iccst/2022-valec-czechia/

Conference

Conference	The 55th Annual International Carnahan Conference on Security Technology (ICCST 2022)
Abbreviated title	IEEE ICCST 2022
Country/Territory	Czech Republic
City	Valez
Period	7/09/22 → 9/09/22
Internet address	https://site.ieee.org/iccst/2022-valec-czechia/

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10.1109/ICCST52959.2022.9896515Licence: Unspecified

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© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/ICCST52959.2022.9896515
Accepted author manuscript, 551 KBLicence: Unspecified

Cite this

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title = "Electronic Number Plate Generation for Performance Evaluation",

abstract = "The authors have been involved in real world analysis of Automatic Number Plate Recognition (ANPR) data and systems particularly for law enforcement applications. As a result of such work with Law Enforcement Agencies, contributions have been made to the revision of the British Standards for ANPR. This led to the research team developing performance evaluation measures from an end-to-end system perspective. One such measure was the generation of synthetic image datasets suitable for ANPR performance evaluation. The prime requirement for any ANPR system is data accuracy. This paper reports the initial work and progress made using defined synthetic images to test and assess ANPR engines using a structured methodology.",

author = "Soodamani Ramalingam and Bill Martin and Michael Rhead, and Robert Gurney",

note = "{\textcopyright} 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/ICCST52959.2022.9896515; The 55th Annual International Carnahan Conference on Security Technology (ICCST 2022) , IEEE ICCST 2022 ; Conference date: 07-09-2022 Through 09-09-2022",

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booktitle = "IEEE International Carnahan Conference on Security Technology (ICCST)",

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Ramalingam, S , Martin, B, Rhead, M & Gurney, R 2022, Electronic Number Plate Generation for Performance Evaluation. in IEEE International Carnahan Conference on Security Technology (ICCST): IEEE ICCST2022. Institute of Electrical and Electronics Engineers (IEEE), Valeč u Hrotovic, Czech Republic, The 55th Annual International Carnahan Conference on Security Technology (ICCST 2022) , Valez, Czech Republic, 7/09/22. https://doi.org/10.1109/ICCST52959.2022.9896515

Electronic Number Plate Generation for Performance Evaluation. / Ramalingam, Soodamani ; Martin, Bill; Rhead, Michael et al.
IEEE International Carnahan Conference on Security Technology (ICCST): IEEE ICCST2022. Valeč u Hrotovic, Czech Republic: Institute of Electrical and Electronics Engineers (IEEE), 2022.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Electronic Number Plate Generation for Performance Evaluation

AU - Ramalingam, Soodamani

AU - Martin, Bill

AU - Rhead,, Michael

AU - Gurney, Robert

N1 - © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/ICCST52959.2022.9896515

PY - 2022/9/26

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N2 - The authors have been involved in real world analysis of Automatic Number Plate Recognition (ANPR) data and systems particularly for law enforcement applications. As a result of such work with Law Enforcement Agencies, contributions have been made to the revision of the British Standards for ANPR. This led to the research team developing performance evaluation measures from an end-to-end system perspective. One such measure was the generation of synthetic image datasets suitable for ANPR performance evaluation. The prime requirement for any ANPR system is data accuracy. This paper reports the initial work and progress made using defined synthetic images to test and assess ANPR engines using a structured methodology.

AB - The authors have been involved in real world analysis of Automatic Number Plate Recognition (ANPR) data and systems particularly for law enforcement applications. As a result of such work with Law Enforcement Agencies, contributions have been made to the revision of the British Standards for ANPR. This led to the research team developing performance evaluation measures from an end-to-end system perspective. One such measure was the generation of synthetic image datasets suitable for ANPR performance evaluation. The prime requirement for any ANPR system is data accuracy. This paper reports the initial work and progress made using defined synthetic images to test and assess ANPR engines using a structured methodology.

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DO - 10.1109/ICCST52959.2022.9896515

M3 - Conference contribution

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BT - IEEE International Carnahan Conference on Security Technology (ICCST)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Valeč u Hrotovic, Czech Republic

T2 - The 55th Annual International Carnahan Conference on Security Technology (ICCST 2022)

Y2 - 7 September 2022 through 9 September 2022

ER -

Electronic Number Plate Generation for Performance Evaluation

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