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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFR97S
Repositorysid.inpe.br/sibgrapi/2017/08.21.21.04
Last Update2017:08.21.21.04.34 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.21.21.04.34
Metadata Last Update2022:06.14.00.08.57 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.27
Citation KeySantosSouzMara:2017:2DDeBo
TitleA 2D Deep Boltzmann Machine for Robust and Fast Vehicle Classification
FormatOn-line
Year2017
Access Date2024, May 02
Number of Files1
Size2284 KiB
2. Context
Author1 Santos, Daniel Felipe Silva
2 Souza, Gustavo Botelho de
3 Marana, Aparecido Nilceu
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressdanielfssantos1@gmail.com
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-21 21:04:34 :: danielfssantos1@gmail.com -> administrator ::
2022-06-14 00:08:57 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsvehicle classification
traffic control
image analysis
deep Boltzmann machines
bilinear projection
AbstractThe visual and automatic classification of vehicles plays an important role in the Transport Area. Besides of security issues, the monitoring of the type of traffic in streets and highways, as well the traffic dynamics over time, allows the optimization of use and of resources related to such public infrastructure. In this work we propose a novel method, called 2D-DBM, for robust and efficient automatic vehicle classification through color images based on a DBM (Deep Boltzmann Machine) combined with bilinear projections. While the DBM training allows a robust initialization of discriminative MLP (Multilayer Perceptron) neural network parameters, the bilinear projection technique can scale down the MLP dimensions, obtaining efficiency while preserving accuracy. The proposed method was assessed on the BIT-Vehicle database, a challenging dataset consisting of frontal images of vehicles collected in a real traffic environment, and compared with a CNN (Convolutional Neural Network) and a traditional DBM (without bilinear projection). The obtained results show that, while keeping the accuracy, the new method significantly reduced the network size and the processing time.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFR97S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFR97S
Languageen
Target FilePID4959939.pdf
User Groupdanielfssantos1@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsaffiliation archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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