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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/3U2N2QH
Repositorysid.inpe.br/sibgrapi/2019/09.10.13.19
Last Update2019:09.10.13.19.44 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2019/09.10.13.19.44
Metadata Last Update2022:06.14.00.09.35 (UTC) administrator
DOI10.1109/SIBGRAPI.2019.00009
Citation KeySantosPireColoPapa:2019:ViSeLe
TitleVideo Segmentation Learning Using Cascade Residual Convolutional Neural Network
FormatOn-line
Year2019
Access Date2024, Apr. 27
Number of Files1
Size918 KiB
2. Context
Author1 Santos, Daniel Felipe Silva
2 Pires, Rafael Gonçalves
3 Colombo, Danilo
4 Papa, João Paulo
Affiliation1 São Paulo State University, Brazil
2 São Paulo State University, Brazil
3 Petroleo Brasileiro S.A. - Petrobras
4 São Paulo State University, Brazil
EditorOliveira, Luciano Rebouças de
Sarder, Pinaki
Lage, Marcos
Sadlo, Filip
e-Mail Addressdanielfssantos1@gmail.com
Conference NameConference on Graphics, Patterns and Images, 32 (SIBGRAPI)
Conference LocationRio de Janeiro, RJ, Brazil
Date28-31 Oct. 2019
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2019-09-10 13:19:44 :: danielfssantos1@gmail.com -> administrator ::
2022-06-14 00:09:35 :: administrator -> danielfssantos1@gmail.com :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsVideo Segmentation
Deep Learning
Foreground Object Detection
Residual Map
AbstractVideo segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such as abrupt changes in weather conditions, illumination issues, shadows, subtle dynamic background motions, and also camouflage effects. In this work, we address such shortcomings by proposing a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process. The main goal is to provide a method capable of generating an accurate foreground detection given a grayscale video. Experiments con- ducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach concerning some state-of-the-art video segmentation techniques, with overall F-measures of 0.9535 and 0.9636 in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a result places the proposed technique amongst the top 3 state-of-the-art video segmentation methods, besides comprising approximately seven times less parameters than its top one counterpart.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2019 > Video Segmentation Learning...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Video Segmentation Learning...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/3U2N2QH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/3U2N2QH
Languageen
Target FilePID6127143.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/3UA4FNL
8JMKD3MGPEW34M/3UA4FPS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2019/10.25.18.30.33 1
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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