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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/438DG35
Repositorysid.inpe.br/sibgrapi/2020/09.11.16.08
Last Update2020:10.01.14.27.23 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.11.16.08.08
Metadata Last Update2022:06.14.00.00.01 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00023
Citation KeySantosPireColoPapa:2020:ScChDe
TitleScene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks
FormatOn-line
Year2020
Access Date2024, Sep. 08
Number of Files1
Size1872 KiB
2. Context
Author1 Santos, Daniel Felipe Silva
2 Pires, Rafael Gonçalves
3 Colombo, Danilo
4 Papa, João Paulo
Affiliation1 São Paulo State University (UNESP)
2 São Paulo State University (UNESP)
3 PETROBRAS - BR
4 São Paulo State University (UNESP)
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressdanielfssantos1@gmail.com
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-10-01 14:27:23 :: danielfssantos1@gmail.com -> administrator :: 2020
2022-06-14 00:00:01 :: administrator -> danielfssantos1@gmail.com :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordschange
detection
learning
multiscale
AbstractScene change detection is an image processing problem related to partitioning pixels of a digital image into foreground and background regions. Mostly, visual knowledge-based computer intelligent systems, like traffic monitoring, video surveillance, and anomaly detection, need to use change detection techniques. Amongst the most prominent detection methods, there are the learning-based ones, which besides sharing similar training and testing protocols, differ from each other in terms of their architecture design strategies. Such architecture design directly impacts on the quality of the detection results, and also in the device resources capacity, like memory. In this work, we propose a novel Multiscale Cascade Residual Convolutional Neural Network that integrates multiscale processing strategy through a Residual Processing Module, with a Segmentation Convolutional Neural Network. Experiments conducted on two different datasets support the effectiveness of the proposed approach, achieving average overall F -measure results of 0.9622 and 0.9664 over Change Detection 2014 and PetrobrasROUTES datasets respectively, besides comprising approximately eight times fewer parameters. Such obtained results place the proposed technique amongst the top four state-of-the-art scene change detection methods.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > Scene Change Detection...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Scene Change Detection...
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4. Conditions of access and use
data URLhttp://sibgrapi.sid.inpe.br/ibi/8JMKD3MGPEW34M/438DG35
zipped data URLhttp://sibgrapi.sid.inpe.br/zip/8JMKD3MGPEW34M/438DG35
Languageen
Target File71.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 Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 28
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy 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
7. Description control
e-Mail (login)danielfssantos1@gmail.com
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