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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PF6LMS
Repositorysid.inpe.br/sibgrapi/2017/08.17.15.31
Last Update2017:08.17.15.31.46 administrator
Metadatasid.inpe.br/sibgrapi/2017/08.17.15.31.46
Metadata Last Update2020:02.19.02.01.22 administrator
Citation KeyCaetanoMeloSantSchw:2017:AcReBa
TitleActivity Recognition based on a Magnitude-Orientation Stream Network
FormatOn-line
Year2017
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size1018 KiB
Context area
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
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-17 15:31:46 :: carlos.caetano@dcc.ufmg.br -> administrator ::
2020-02-19 02:01:22 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
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.
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data URLhttp://urlib.net/rep/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
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PJT9LS
8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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