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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/47MHK5B
Repositorysid.inpe.br/sibgrapi/2022/09.26.20.57
Last Update2022:09.26.20.57.05 (UTC) flso@cin.ufpe.br
Metadata Repositorysid.inpe.br/sibgrapi/2022/09.26.20.57.05
Metadata Last Update2023:05.23.04.20.43 (UTC) administrator
DOI10.1109/SIBGRAPI55357.2022.9991761
Citation KeyOliveiraMaBaSoFrVi:2022:FiCaRe
TitleFine-grained cars recognition using deep convolutional neural networks
Short TitleFine-grained cars recognition using deep convolutional neural networks
FormatOn-line
Year2022
Access Date2024, Apr. 28
Number of Files1
Size940 KiB
2. Context
Author1 Oliveira, Franklin Lazaro Santos de
2 Macena, Arianne Santos da
3 Barbosa, Otávio Azevedo de Carvalho Kamel
4 Souza, Wesley
5 Freitas, Nicksson Ckayo Arrais de
6 Vinuto, Tiago Da Silva
Affiliation1 Federal University of Pernambuco
2 Federal University of Pernambuco
3 Federal University of Pernambuco
4 Federal University of Pernambuco
5 SiDi
6 SiDi
e-Mail Addressflso@cin.ufpe.br
Conference NameConference on Graphics, Patterns and Images, 35 (SIBGRAPI)
Conference LocationNatal, RN
Date24-27 Oct. 2022
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2022-09-26 20:57:05 :: flso@cin.ufpe.br -> administrator ::
2023-05-23 04:20:43 :: administrator -> :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsintelligent transportation systems
fine-grained classification
car recognition
AbstractPopulation growth and the high concentration of vehicles on urban roads have been negatively impacting urban mobility and the global environment, since the primary transportation modes occupy a lot of space on the streets and are one of the main polluting gas emitters. In this context of inefficient urban mobility and unsustainability, the Intelligent Transportation Systems (ITS) aims to solve or minimize urban traffic issues. ITS are also widely used in applications focused on traffic safety, such as vehicle recognition related to a traffic or law violation. For this task, the fine-grained vehicle classification technique is used mainly by advances in computer vision and deep learning. However, identifying vehicles by the model can be a problem because the same vehicle can be easily misclassified when observed from different perspectives, with different colors, or by similar models. Knowing these inherent issues from vehicle recognition tasks, Deep Convolutional Neural Networks (DCNNs) are commonly used due to their ability to extract features from images. In that regard, the goal of this paper is to evaluate some state of art DCNNs architectures, conducting experiments with three different datasets to identify which architectures have the best performance metrics in the refined car classification task within ITS context.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2022 > Fine-grained cars recognition using deep convolutional neural networks
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/47MHK5B
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/47MHK5B
Languageen
Target Fileoliveira-20.pdf
User Groupflso@cin.ufpe.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/495MHJ8
Citing Item Listsid.inpe.br/sibgrapi/2023/05.19.12.10 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition editor electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session sponsor subject tertiarymark type url versiontype volume


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