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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFGBPP
Repositorysid.inpe.br/sibgrapi/2017/08.19.20.23
Last Update2017:08.19.20.23.01 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.19.20.23.01
Metadata Last Update2022:06.14.00.08.50 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.49
Citation KeySouzaPedr:2017:DeViEv
TitleDetection of Violent Events in Video Sequences based on Census Transform Histogram
FormatOn-line
Year2017
Access Date2024, May 01
Number of Files1
Size1499 KiB
2. Context
Author1 Souza, Felipe de
2 Pedrini, Helio
Affiliation1 Institute of Computing, University of Campinas (UNICAMP)
2 Institute of Computing, University of Campinas (UNICAMP)
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addresshelio@ic.unicamp.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-19 20:23:01 :: helio@ic.unicamp.br -> administrator ::
2022-06-14 00:08:50 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsvideo analysis
violent detection
surveillance systems
anomalous events
AbstractVideo surveillance systems have enabled the monitoring of complex events in several places, such as airports, banks, streets, schools, industries, among others. Due to the massive amount of multimedia data acquired by video cameras, traditional visual inspection by human operators is a very tedious and time consuming task, whose performance is affected by fatigue and stress. A challenge is to develop intelligent video systems capable of automatically analyzing long sequences of videos from a large number of cameras. This work describes and evaluates the use of CENTRIST-based features to identify violence context from video scenes. Experimental results demonstrate the effectiveness of our method when applied to two public benchmarks, Violent Flows and Hockey Fights datasets.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Detection of Violent...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Detection of Violent...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFGBPP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFGBPP
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 Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 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


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