License Plate Recognition and Super-resolution from Low-Resolution Videos by Convolutional Neural Networks

Result code in 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>

Result on the web

<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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Result language

angličtina

Original language name

License Plate Recognition and Super-resolution from Low-Resolution Videos by Convolutional Neural Networks

Original language description

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.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA16-05872S" target="_blank" >GA16-05872S: Probabilistic Graphical Models and Deep Learning</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů