Enhance of License Plate Matching Procedure Quality Using Augmented Probability Matrix Method
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F25%3A00384121" target="_blank" >RIV/68407700:21260/25:00384121 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1109/SCSP65598.2025.11037719" target="_blank" >https://doi.org/10.1109/SCSP65598.2025.11037719</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SCSP65598.2025.11037719" target="_blank" >10.1109/SCSP65598.2025.11037719</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Enhance of License Plate Matching Procedure Quality Using Augmented Probability Matrix Method
Popis výsledku v původním jazyce
The issue of traffic data quality is an important part of the Smart City concept. Accurate and high quality traffic data serves cities not only for detailed traffic insights, online and offline traffic management, but also for future decision making on the im-plementation of strategic traffic measures. This paper focuses specifically on the processing and evaluation of data from constantly developing license plate recognition (LPR) systems and aims to develop a method that allows comparing text strings of license plate characters from different vehicle records and, without knowing the real (correct) shape of the license plate, determine whether the compared records belong to the same vehi-cle or not - in other words, whether they should be matched. The paper builds on previous research and a quality improve-ment method based on a probabilistic model for two measurement profiles. This paper focuses on the application, actualization and extension of the mentioned method to LPR systems with arbitrary number of m
Název v anglickém jazyce
Enhance of License Plate Matching Procedure Quality Using Augmented Probability Matrix Method
Popis výsledku anglicky
The issue of traffic data quality is an important part of the Smart City concept. Accurate and high quality traffic data serves cities not only for detailed traffic insights, online and offline traffic management, but also for future decision making on the im-plementation of strategic traffic measures. This paper focuses specifically on the processing and evaluation of data from constantly developing license plate recognition (LPR) systems and aims to develop a method that allows comparing text strings of license plate characters from different vehicle records and, without knowing the real (correct) shape of the license plate, determine whether the compared records belong to the same vehi-cle or not - in other words, whether they should be matched. The paper builds on previous research and a quality improve-ment method based on a probabilistic model for two measurement profiles. This paper focuses on the application, actualization and extension of the mentioned method to LPR systems with arbitrary number of m
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10700 - Other natural sciences
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
2025 Smart City Symposium Prague - IEEE proceedings
ISBN
979-8-3315-2551-4
ISSN
2831-5618
e-ISSN
2691-3666
Počet stran výsledku
7
Strana od-do
—
Název nakladatele
IEEE Press
Místo vydání
New York
Místo konání akce
Prague
Datum konání akce
29. 5. 2025
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—