@InProceedings{GonçalvesDinLarMenSch:2018:ReAuLi,
author = "Gon{\c{c}}alves, Gabriel Resende and Diniz, Matheus Alves and
Laroca, Rayson and Menotti, David and Schwartz, William Robson",
affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal
de Minas Gerais} and {Universidade Federal do Paran{\'a}} and
{Universidade Federal do Paran{\'a}} and {Universidade Federal de
Minas Gerais}",
title = "Real-time Automatic License Plate Recognition Through Deep
Multi-Task Networks",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "automatic license plate recognition, deep learning, multi-task
learning, traffic surveillance, real-time.",
abstract = "With the increasing number of cameras available in the cities,
video traffic analysis can provide useful insights for the
transportation segment. One of such analysis is the Automatic
License Plate Recognition (ALPR). Previous approaches divided this
task into several cascaded subtasks, i.e., vehicle location,
license plate detection, character segmentation and optical
character recognition. However, since each task has its own
accuracy, the error propagation between each subtask is
detrimental to the final accuracy. Therefore, focusing on the
reduction of error propagation, we propose a technique that is
able to perform ALPR using only two deep networks, the first
performs license plate detection (LPD) and the second performs
license plate recognition (LPR). The latter does not execute
explicit character segmentation, which reduces significantly the
error propagation. As these deep networks need a large number of
samples to converge, we develop new data augmentation techniques
that allow them to reach their full potential as well as a new
dataset to train and evaluate ALPR approaches. According to
experimental results, our approach is able to achieve
state-of-the-art results in the SSIG-SegPlate dataset, reaching
improvements up to 1.4 percentage point when compared to the best
baseline. Furthermore, the approach is also able to perform in
real time even in scenarios where many plates are present at the
same frame, reaching significantly higher frame rates when
compared with previously proposed approaches.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
doi = "10.1109/SIBGRAPI.2018.00021",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00021",
language = "en",
ibi = "8JMKD3MGPAW/3RPASL5",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3RPASL5",
targetfile = "paper44.pdf",
urlaccessdate = "2024, Apr. 29"
}