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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M92PCE
Repositorysid.inpe.br/sibgrapi/2016/08.12.16.50
Last Update2016:08.12.16.50.05 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/08.12.16.50.05
Metadata Last Update2022:05.18.22.21.07 (UTC) administrator
Citation KeyAlcantaraPedr:2016:HuAcId
TitleHuman Action Identification in Videos using Descriptor with Autonomous Fragments and Multilevel Prediction
FormatOn-line
Year2016
Access Date2024, Apr. 28
Number of Files1
Size1145 KiB
2. Context
Author1 Alcantara, Marlon Fernandes de
2 Pedrini, Hélio
Affiliation1 Universidade Estadual de Campinas
2 Universidade Estadual de Campinas
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addressmarlonmfa@gmail.com
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2016-08-12 16:50:05 :: marlonmfa@gmail.com -> administrator ::
2022-05-18 22:21:07 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsaction recognition
machine learning
computer vision
AbstractRecent technological advances have provided devices with high processing power and storage capacities. Video cameras are found in several places, such as banks, airports, schools, supermarkets, streets, homes and industries. However, most of the video analysis tasks are still performed by human operators influenced by factors such stress and fatigue. This work proposes and evaluates a methodology for identifying common human actions by means of a CMSIP descriptor applied to a multilevel prediction scheme with retraining. The approach is built by dividing the descriptor into portions considered and interpreted independently by following distinct ways on the classification model, such that, a central mechanism will be responsible for deciding which action is being observed. Our method has proved to be fast and with accuracy compatible to the state-of-the-art on known public data sets. Furthermore, the developed prototype demonstrated to be a promising tool for real-time applications.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2016 > Human Action Identification...
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/3M92PCE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M92PCE
Languageen
Target Filepaper.pdf
User Groupmarlonmfa@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 7
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi 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 versiontype volume


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