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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PF6LMS
Repositorysid.inpe.br/sibgrapi/2017/08.17.15.31
Last Update2017:08.17.15.31.46 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.17.15.31.46
Metadata Last Update2022:06.14.00.08.44 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.13
Citation KeyCaetanoMeloSantSchw:2017:AcReBa
TitleActivity Recognition based on a Magnitude-Orientation Stream Network
FormatOn-line
Year2017
Access Date2024, Apr. 29
Number of Files1
Size1018 KiB
2. Context
Author1 Caetano, Carlos
2 Melo, Victor H. C. de
3 Santos, Jefersson A. dos
4 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
4 Universidade Federal de Minas Gerais
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 Addresscarlos.caetano@dcc.ufmg.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)2017-08-17 15:31:46 :: carlos.caetano@dcc.ufmg.br -> administrator ::
2022-06-14 00:08:44 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsMagnitude
Orientation
Stream Network
Convolutional Neural Networks
AbstractThe temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple nonlinear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Experimental results, carried on two well-known datasets (HMDB51 and UCF101), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Activity Recognition based...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Activity Recognition based...
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source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PF6LMS
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PF6LMS
Languageen
Target Filemain Certified by IEEE PDF eXpress.pdf
User Groupcarlos.caetano@dcc.ufmg.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 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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