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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/3U2DL8E
Repositorysid.inpe.br/sibgrapi/2019/09.08.15.38
Last Update2019:09.08.16.52.22 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2019/09.08.15.38.29
Metadata Last Update2022:06.14.00.09.30 (UTC) administrator
DOI10.1109/SIBGRAPI.2019.00010
Citation KeyCarneiroSilvGuimPedr:2019:FiDeVi
TitleFight Detection in Video Sequences Based on Multi-Stream Convolutional Neural Networks
FormatOn-line
Year2019
Access Date2024, Oct. 15
Number of Files1
Size979 KiB
2. Context
Author1 Carneiro, Sarah Almeida
2 Silva, Gabriel Pellegrino da
3 Guimarães, Silvio Jamil F.
4 Pedrini, Helio
Affiliation1 Institute of Computing, University of Campinas
2 Institute of Computing, University of Campinas
3 Computer Science Department, Pontifical Catholic University of Minas Gerais
4 Institute of Computing, University of Campinas
EditorOliveira, Luciano Rebouças de
Sarder, Pinaki
Lage, Marcos
Sadlo, Filip
e-Mail Addresshelio@ic.unicamp.br
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)2019-09-08 16:52:22 :: helio@ic.unicamp.br -> administrator :: 2019
2022-06-14 00:09:30 :: administrator -> helio@ic.unicamp.br :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsFight detection
convolutional neural networks
video analysis
AbstractSurveillance has been gradually correlating itself to forensic computer technologies. The use of machine learning techniques made possible the better interpretation of human actions, as well as faster identification of anomalous event outbursts. There are many studies regarding this field of expertise. The best results reported in the literature are from works related to deep learning approaches. Therefore, this study aimed to use a deep learning model based on a multi-stream and high level hand-crafted descriptors to be able to address the issue of fight detection in videos. In this work, we focused on the use of a multi-stream of VGG-16 networks and the investigation of conceivable feature descriptors of a video's spatial, temporal, rhythmic and depth information. We validated our method in two commonly used datasets, aimed at fight detection, throughout the literature. Experimentation has demonstrated that the association of correlated information with a multi-stream strategy increased the classification of our deep learning approach, hence, the use of complementary features can yield interesting outputs that are superior than other previous studies.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2019 > Fight Detection in...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Fight Detection in...
doc Directory Contentaccess
source Directory Content
paper.pdf 08/09/2019 12:38 978.3 KiB 
agreement Directory Content
agreement.html 08/09/2019 12:38 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/3U2DL8E
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/3U2DL8E
Languageen
Target Filepaper.pdf
User Grouphelio@ic.unicamp.br
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 23
sid.inpe.br/sibgrapi/2022/06.10.21.49 4
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)helio@ic.unicamp.br
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