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Reference TypeConference Proceedings
Last Update2018: administrator
Metadata Last Update2020: administrator
Citation KeyGonçalvesDinLarMenSch:2018:ReAuLi
TitleReal-time Automatic License Plate Recognition Through Deep Multi-Task Networks
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 02
Number of Files1
Size4161 KiB
Context area
Author1 Gonçalves, Gabriel Resende
2 Diniz, Matheus Alves
3 Laroca, Rayson
4 Menotti, David
5 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal do Paraná
4 Universidade Federal do Paraná
5 Universidade Federal de Minas Gerais
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-09-04 14:03:57 :: -> administrator :: 2018
2018-09-04 16:41:03 :: administrator -> :: 2018
2018-09-07 22:19:08 :: -> administrator :: 2018
2020-02-19 03:10:44 :: administrator -> :: 2018
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Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Content TypeExternal Contribution
Tertiary TypeFull Paper
Keywordsautomatic license plate recognition, deep learning, multi-task learning, traffic surveillance, real-time.
AbstractWith 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.
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Next Higher Units8JMKD3MGPAW/3RPADUS
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