License Plate Recognition and Super-resolution from Low-Resolution Videos by Convolutional Neural Networks
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00324562" target="_blank" >RIV/68407700:21230/18:00324562 - isvavai.cz</a>
Výsledek na webu
<a href="http://www.bmvc2018.org/contents/papers/0537.pdf" target="_blank" >http://www.bmvc2018.org/contents/papers/0537.pdf</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
License Plate Recognition and Super-resolution from Low-Resolution Videos by Convolutional Neural Networks
Popis výsledku v původním jazyce
The paper proposes Convolutional Neural Network (CNN) for License Plate Recognition (LPR) from low-resolution videos. The CNN accepts arbitrary long sequence of geometrically registered license plate (LP) images and outputs a distribution over a set of strings with an admissible length. Evaluation on 31k low-resolution videos shows that the proposed CNN significantly outperforms both baseline methods and humans by a large margin. Our second contribution is a CNN based super-resolution generator of LP images. The generator converts input low-resolution LP image into its high-resolution counterpart which i) preserves the structure of the input and ii) depicts a string that was previously recognized from video.
Název v anglickém jazyce
License Plate Recognition and Super-resolution from Low-Resolution Videos by Convolutional Neural Networks
Popis výsledku anglicky
The paper proposes Convolutional Neural Network (CNN) for License Plate Recognition (LPR) from low-resolution videos. The CNN accepts arbitrary long sequence of geometrically registered license plate (LP) images and outputs a distribution over a set of strings with an admissible length. Evaluation on 31k low-resolution videos shows that the proposed CNN significantly outperforms both baseline methods and humans by a large margin. Our second contribution is a CNN based super-resolution generator of LP images. The generator converts input low-resolution LP image into its high-resolution counterpart which i) preserves the structure of the input and ii) depicts a string that was previously recognized from video.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/GA16-05872S" target="_blank" >GA16-05872S: Pravděpodobnostní grafové modely a hluboké učení</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2018
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů