1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPEW34M/3U2N2QH |
Repository | sid.inpe.br/sibgrapi/2019/09.10.13.19 |
Last Update | 2019:09.10.13.19.44 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2019/09.10.13.19.44 |
Metadata Last Update | 2022:06.14.00.09.35 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2019.00009 |
Citation Key | SantosPireColoPapa:2019:ViSeLe |
Title | Video Segmentation Learning Using Cascade Residual Convolutional Neural Network |
Format | On-line |
Year | 2019 |
Access Date | 2024, Apr. 29 |
Number of Files | 1 |
Size | 918 KiB |
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2. Context | |
Author | 1 Santos, Daniel Felipe Silva 2 Pires, Rafael Gonçalves 3 Colombo, Danilo 4 Papa, João Paulo |
Affiliation | 1 São Paulo State University, Brazil 2 São Paulo State University, Brazil 3 Petroleo Brasileiro S.A. - Petrobras 4 São Paulo State University, Brazil |
Editor | Oliveira, Luciano Rebouças de Sarder, Pinaki Lage, Marcos Sadlo, Filip |
e-Mail Address | danielfssantos1@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 32 (SIBGRAPI) |
Conference Location | Rio de Janeiro, RJ, Brazil |
Date | 28-31 Oct. 2019 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full Paper |
History (UTC) | 2019-09-10 13:19:44 :: danielfssantos1@gmail.com -> administrator :: 2022-06-14 00:09:35 :: administrator -> danielfssantos1@gmail.com :: 2019 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | Video Segmentation Deep Learning Foreground Object Detection Residual Map |
Abstract | Video 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 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2019 > Video Segmentation Learning... |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Video Segmentation Learning... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/3U2N2QH |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/3U2N2QH |
Language | en |
Target File | PID6127143.pdf |
User Group | danielfssantos1@gmail.com |
Visibility | shown |
Update Permission | not transferred |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/3UA4FNL 8JMKD3MGPEW34M/3UA4FPS 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2019/10.25.18.30.33 1 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | 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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7. Description control | |
e-Mail (login) | danielfssantos1@gmail.com |
update | |
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