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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/43BDCD8
Repositorysid.inpe.br/sibgrapi/2020/09.30.02.16
Last Update2020:09.30.02.16.07 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.30.02.16.07
Metadata Last Update2022:06.14.00.00.13 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00017
Citation KeySantosAlme:2020:FaAcCo
TitleFaster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain
FormatOn-line
Year2020
Access Date2024, Dec. 21
Number of Files1
Size2817 KiB
2. Context
Author1 Santos, Samuel Felipe dos
2 Almeida, Jurandy
Affiliation1 Universidade Federal de São Paulo - UNIFESP
2 Universidade Federal de São Paulo - UNIFESP
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressjurandy.almeida@unifesp.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-30 02:16:07 :: jurandy.almeida@unifesp.br -> administrator ::
2022-06-14 00:00:13 :: administrator -> jurandy.almeida@unifesp.br :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsaction recognition
convolutional neural network
compressed-domain processing
frequency domain
AbstractHuman action recognition has become one of the most active field of research in computer vision due to its wide range of applications, like surveillance, medical, industrial environments, smart homes, among others. Recently, deep learning has been successfully used to learn powerful and interpretable features for recognizing human actions in videos. Most of the existing deep learning approaches have been designed for processing video information as RGB image sequences. For this reason, a preliminary decoding process is required, since video data are often stored in a compressed format. However, a high computational load and memory usage is demanded for decoding a video. To overcome this problem, we propose a deep neural network capable of learning straight from compressed video. Our approach was evaluated on two public benchmarks, the UCF-101 and HMDB-51 datasets, demonstrating comparable recognition performance to the state-of-the-art methods, with the advantage of running up to 2 times faster in terms of inference speed.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > Faster and Accurate...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Faster and Accurate...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/43BDCD8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/43BDCD8
Languageen
Target FilePID6630911.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/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 47
sid.inpe.br/sibgrapi/2022/06.10.21.49 8
sid.inpe.br/banon/2001/03.30.15.38.24 2
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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