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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/3U2JR42
Repositorysid.inpe.br/sibgrapi/2019/09.09.19.57
Last Update2019:09.09.19.57.53 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2019/09.09.19.57.53
Metadata Last Update2022:06.14.00.09.33 (UTC) administrator
DOI10.1109/SIBGRAPI.2019.00011
Citation KeyCaetanoBrémSchw:2019:SkImRe
TitleSkeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints
FormatOn-line
Year2019
Access Date2024, Apr. 28
Number of Files1
Size2713 KiB
2. Context
Author1 Caetano, Carlos
2 Brémond, François
3 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 INRIA
3 Universidade Federal de Minas Gerais
EditorOliveira, Luciano Rebouças de
Sarder, Pinaki
Lage, Marcos
Sadlo, Filip
e-Mail Addresscarlos.a.caetano.jr@gmail.com
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 19:57:53 :: carlos.a.caetano.jr@gmail.com -> administrator ::
2022-06-14 00:09:33 :: administrator -> carlos.a.caetano.jr@gmail.com :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsskeleton image representation
convolutional neural network (CNN)
3D action recognition
AbstractIn the last years, the computer vision research community has studied on how to model temporal dynamics in videos to employ 3D human action recognition. To that end, two main baseline approaches have been researched: (i) Recurrent Neural Networks (RNNs) with Long-Short Term Memory (LSTM); and (ii) skeleton image representations used as input to a Convolutional Neural Network (CNN). Although RNN approaches present excellent results, such methods lack the ability to efficiently learn the spatial relations between the skeleton joints. On the other hand, the representations used to feed CNN approaches present the advantage of having the natural ability of learning structural information from 2D arrays (i.e., they learn spatial relations from the skeleton joints). To further improve such representations, we introduce the \metodosigla, a novel skeleton image representation to be used as input to CNNs. The proposed representation has the advantage of combining the use of reference joints and a tree structure skeleton. While the former incorporates different spatial relationships between the joints, the latter preserves important spatial relations by traversing a skeleton tree with a depth-first order algorithm. Experimental results demonstrate the effectiveness of the proposed representation for 3D action recognition on two datasets achieving state-of-the-art results on the recent NTU RGB+D~120 dataset.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2019 > Skeleton Image Representation...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Skeleton Image Representation...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/3U2JR42
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/3U2JR42
Languageen
Target FileSIBGRAPI2019_submitted_camera_ready.pdf
User Groupcarlos.a.caetano.jr@gmail.com
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)carlos.a.caetano.jr@gmail.com
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