Geometric Alignment by Deep Learning for Recognition of Challenging License Plates

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU130797" target="_blank" >RIV/00216305:26230/18:PU130797 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8569259" target="_blank" >https://ieeexplore.ieee.org/document/8569259</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ITSC.2018.8569259" target="_blank" >10.1109/ITSC.2018.8569259</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Geometric Alignment by Deep Learning for Recognition of Challenging License Plates

Popis výsledku v původním jazyce

In this paper, we explore the problem of license plate recognition in-the-wild (in the meaning of capturing data in unconstrained conditions, taken from arbitrary viewpoints and distances). We propose a method for automatic license plate recognition in-the-wild based on a geometric alignment of license plates as a preceding step for holistic license plate recognition. The alignment is done by a Convolutional Neural Network that estimates control points for rectifying the image and the following rectification step is formulated so that the whole alignment and recognition process can be assembled into one computational graph of a contemporary neural network framework, such as Tensorflow. The experiments show that the use of the aligner helps the recognition considerably: the error rate dropped from 9.6 % to 2.1 % on real-life images of license plates. The experiments also show that the solution is fast - it is capable of real-time processing even on an embedded and low-power platform (Jetson TX2). We collected and annotated a dataset of license plates called CamCar6k, containing 6,064 images with annotated corner points and ground truth texts. We make this dataset publicly available.

Název v anglickém jazyce

Geometric Alignment by Deep Learning for Recognition of Challenging License Plates

Popis výsledku anglicky

In this paper, we explore the problem of license plate recognition in-the-wild (in the meaning of capturing data in unconstrained conditions, taken from arbitrary viewpoints and distances). We propose a method for automatic license plate recognition in-the-wild based on a geometric alignment of license plates as a preceding step for holistic license plate recognition. The alignment is done by a Convolutional Neural Network that estimates control points for rectifying the image and the following rectification step is formulated so that the whole alignment and recognition process can be assembled into one computational graph of a contemporary neural network framework, such as Tensorflow. The experiments show that the use of the aligner helps the recognition considerably: the error rate dropped from 9.6 % to 2.1 % on real-life images of license plates. The experiments also show that the solution is fast - it is capable of real-time processing even on an embedded and low-power platform (Jetson TX2). We collected and annotated a dataset of license plates called CamCar6k, containing 6,064 images with annotated corner points and ground truth texts. We make this dataset publicly available.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

ISBN

978-1-72810-321-1

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

3524-3529

Název nakladatele

IEEE Intelligent Transportation Systems Society

Místo vydání

Lahaina, Maui

Místo konání akce

Maui, Hawaii

Datum konání akce

4. 11. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000457881303079