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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFGFL8
Repositorysid.inpe.br/sibgrapi/2017/08.19.21.10
Last Update2017:08.19.21.10.29 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.19.21.10.29
Metadata Last Update2022:06.14.00.08.50 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.36
Citation KeyFavarettoDihMusVilCos:2017:UsBiFi
TitleUsing Big Five Personality Model to Detect Cultural Aspects in Crowds
FormatOn-line
Year2017
Access Date2024, Apr. 20
Number of Files1
Size3497 KiB
2. Context
Author1 Favaretto, Rodolfo Migon
2 Dihl, Leandro
3 Musse, Soraia Raupp
4 Vilanova, Felipe
5 Costa, Angelo Brandelli
Affiliation1 Pontifical Catholic University of Rio Grande do Sul, Graduate Studies on Computer Science
2 Pontifical Catholic University of Rio Grande do Sul, Graduate Studies on Computer Science
3 Pontifical Catholic University of Rio Grande do Sul, Graduate Studies on Computer Science
4 Pontifical Catholic University of Rio Grande do Sul, Graduate Studies on Psychology
5 Pontifical Catholic University of Rio Grande do Sul, Graduate Studies on Psychology
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 Addressrodolfo.favaretto@gmail.com
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 21:10:29 :: rodolfo.favaretto@gmail.com -> 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
KeywordsCultural aspects
Big Five personality
Crowds
Computer vision
AbstractThe use of information technology in the study of human behavior is a subject of great scientific interest. Cultural and personality aspects are factors that influence how people interact with one another in a crowd. This paper presents a methodology to detect cultural characteristics of crowds in video sequences. Based on filmed sequences, pedestrians are detected, tracked and characterized. Such information is then used to find out cultural differences in those videos, based on the Big-five personality model. Regarding cultural differences of each country, results indicate that this model generates coherent information when compared to data provided in literature.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Using Big Five...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Using Big Five...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 19/08/2017 18:10 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFGFL8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFGFL8
Languageen
Target FilePID4958091.pdf
User Grouprodolfo.favaretto@gmail.com
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 5
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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