Close
Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3EEH5B2
Repositorysid.inpe.br/sibgrapi/2013/07.11.04.39
Last Update2013:07.11.04.39.29 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2013/07.11.04.39.29
Metadata Last Update2022:07.30.18.33.24 (UTC) administrator
DOI10.1109/SIBGRAPI.2013.19
Citation KeySadMotMacVieAra:2013:TeMoDe
TitleA Tensor Motion Descriptor Based on Multiple Gradient Estimators
FormatOn-line.
Year2013
Access Date2024, Apr. 28
Number of Files1
Size241 KiB
2. Context
Author1 Sad, Dhiego
2 Mota, Virgínia Fernandes
3 Maciel, Luiz Maurílio
4 Vieira, Marcelo Bernardes
5 Araújo, Arnaldo de Albuquerque
Affiliation1 Universidade Federal de Juiz de Fora
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Juiz de Fora
4 Universidade Federal de Juiz de Fora
5 Universidade Federal de Minas Gerais
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
e-Mail Addressvirginiafernandesmota@gmail.com
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
Date5-8 Aug. 2013
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2013-07-11 04:39:29 :: virginiafernandesmota@gmail.com -> administrator ::
2022-07-30 18:33:24 :: administrator -> :: 2013
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsMultifilter analysis
Motion descriptor
Orientation tensor
Human action recognition
AbstractThis work presents a novel approach for motion description in videos using multiple band-pass filters which act as first order derivative estimators. The filters response on each frame are coded into individual histograms of gradients to reduce their dimensionality. They are combined using orientation tensors. No local features are extracted and no learning is performed, i.e., the descriptor depends uniquely on the input video. Motion description can be enhanced even using multiple filters with similar or overlapping frequency response. For the problem of human action recognition using the KTH database, our descriptor achieved the recognition rate of 93.3% using three Daubechies filters, one extra filter designed to correlate them, two-fold protocol and a SVM classifier. It is superior to most global descriptor approaches and fairly comparable to the state- of-the-art methods.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2013 > A Tensor Motion...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > A Tensor Motion...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 11/07/2013 01:39 0.7 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3EEH5B2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3EEH5B2
Languageen
Target Filepaper_sad_114944.pdf
User Groupvirginiafernandesmota@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SLB4P
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.04.02 8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage 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


Close