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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/3U2KG6S
Repositorysid.inpe.br/sibgrapi/2019/09.09.23.37
Last Update2019:09.09.23.37.24 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2019/09.09.23.37.24
Metadata Last Update2022:06.14.00.09.34 (UTC) administrator
DOI10.1109/SIBGRAPI.2019.00012
Citation KeySantosSebeAlme:2019:AcReCo
TitleCV-C3D: Action Recognition on Compressed Videos with Convolutional 3D Networks
FormatOn-line
Year2019
Access Date2024, Apr. 28
Number of Files1
Size8878 KiB
2. Context
Author1 Santos, Samuel Felipe dos
2 Sebe, Nicu
3 Almeida, Jurandy
Affiliation1 Universidade Federal de São Paulo - UNIFESP, Brazil
2 University of Trento - UniTn, Italy
3 Universidade Federal de São Paulo - UNIFESP, Brazil
EditorOliveira, Luciano Rebouças de
Sarder, Pinaki
Lage, Marcos
Sadlo, Filip
e-Mail Addressjurandy.almeida@unifesp.br
Conference NameConference on Graphics, Patterns and Images, 32 (SIBGRAPI)
Conference LocationRio de Janeiro, RJ, Brazil
Date28-31 Oct. 2019
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2019-09-09 23:37:24 :: jurandy.almeida@unifesp.br -> administrator ::
2022-06-14 00:09:34 :: administrator -> jurandy.almeida@unifesp.br :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordscomputer vision
action recognition
deep learning
compressed domain
efficiency
AbstractAction recognition in videos has gained substantial attention from the computer vision community due to the wide range of possible applications. Recent works have addressed this problem with deep learning methods. The main limitation of existing approaches is their difficulty to learn temporal dynamics due to the high computational load demanded for processing huge amounts of data required to train a model. To overcome this problem, we propose a Compressed Video Convolutional 3D network (CV-C3D). It exploits information from the compressed representation of a video in order to avoid the high computational cost for fully decoding the video stream. The speed up of the computation enables our network to use 3D convolutions for capturing the temporal context efficiently. Our network has the lowest computational complexity among all the compared approaches. Results of our approach in the task of action recognition on two public benchmarks, UCF-101 and HMDB-51, were comparable to the baselines, with the advantage of running at faster inference speed.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2019 > CV-C3D: Action Recognition...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > CV-C3D: Action Recognition...
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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/8JMKD3MGPEW34M/3U2KG6S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/3U2KG6S
Languageen
Target File118paper.pdf
User Groupjurandy.almeida@unifesp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/3UA4FNL
8JMKD3MGPEW34M/3UA4FPS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2019/10.25.18.30.33 1
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
7. Description control
e-Mail (login)jurandy.almeida@unifesp.br
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