Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPEW34M/438DG35 |
Repository | sid.inpe.br/sibgrapi/2020/09.11.16.08 |
Last Update | 2020:10.01.14.27.23 danielfssantos1@gmail.com |
Metadata | sid.inpe.br/sibgrapi/2020/09.11.16.08.08 |
Metadata Last Update | 2020:10.28.20.46.47 administrator |
Citation Key | SantosPireColoPapa:2020:ScChDe |
Title | Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks  |
Format | On-line |
Year | 2020 |
Date | Nov. 7-10, 2020 |
Access Date | 2021, Jan. 19 |
Number of Files | 1 |
Size | 1872 KiB |
Context area | |
Author | 1 Santos, Daniel Felipe Silva 2 Pires, Rafael Gonçalves 3 Colombo, Danilo 4 Papa, João Paulo |
Affiliation | 1 São Paulo State University (UNESP) 2 São Paulo State University (UNESP) 3 PETROBRAS - BR 4 São Paulo State University (UNESP) |
Editor | Musse, Soraia Raupp Cesar Junior, Roberto Marcondes Pelechano, Nuria Wang, Zhangyang (Atlas) |
e-Mail Address | danielfssantos1@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 33 (SIBGRAPI) |
Conference Location | Virtual |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2020-10-01 14:27:23 :: danielfssantos1@gmail.com -> administrator :: 2020 2020-10-28 20:46:47 :: administrator -> danielfssantos1@gmail.com :: 2020 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | change, detection, learning, multiscale. |
Abstract | Scene 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. |
source Directory Content | 71.pdf | 28/09/2020 13:13 | 1.8 MiB | |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPEW34M/438DG35 |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/438DG35 |
Language | en |
Target File | 71.pdf |
User Group | danielfssantos1@gmail.com |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/43G4L9S |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
Description control area | |
e-Mail (login) | danielfssantos1@gmail.com |
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