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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/3U2KT7L
Repositorysid.inpe.br/sibgrapi/2019/09.10.01.51
Last Update2019:09.10.01.51.16 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2019/09.10.01.51.16
Metadata Last Update2022:06.14.00.09.35 (UTC) administrator
DOI10.1109/SIBGRAPI.2019.00031
Citation KeyBastosMeloSchw:2019:MuReRe
TitleMulti-Loss Recurrent Residual Networks for Gesture Detection and Recognition
FormatOn-line
Year2019
Access Date2024, Apr. 28
Number of Files1
Size863 KiB
2. Context
Author1 Bastos, Igor Leonardo Oliveira
2 Melo, Victor Hugo Cunha de
3 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
EditorOliveira, Luciano Rebouças de
Sarder, Pinaki
Lage, Marcos
Sadlo, Filip
e-Mail Addressigorcrexito@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)2019-09-10 01:51:16 :: igorcrexito@gmail.com -> administrator ::
2022-06-14 00:09:35 :: administrator -> igorcrexito@gmail.com :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsGesture detection
gesture recognition
recurrent models
multi-task
AbstractCommunication through gestures plays a relevant role in human life, in which a non-verbal language is used to propagate information among individuals. To recognize gestures, computers need to represent and interpret human appearance and motion, involving hands, arms, face, head and/or body, in a mathematical sense. Despite the high applicability in different contexts, most gesture recognition approaches in literature are not designed to deal with unsegmented videos. That is, most approaches do not temporally detect when a gesture occurs, which prevents to explore correlations between detection and recognition tasks, besides their application on real-world scenarios. In this sense, we propose the Multi-Loss Recurrent Residual Network (MLRRN), a multi-task based approach that performs both the recognition and temporal detection of gestures at once. It employs a dual loss function which takes into account the class assignment of each frame of a video to a gesture class and also determines the frame interval associated to each gesture. Our model counts with a dual input, gathering information from appearance and human pose on frames, besides bidirectional recurrent layers and residual modules. According to experiments conducted on ChaLearn Montalbano and ChaLearn ConGD datasets, our approach achieves results comparable to state-of-the-art methods considering average temporal Jaccard metric.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2019 > Multi-Loss Recurrent Residual...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Multi-Loss Recurrent Residual...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/3U2KT7L
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/3U2KT7L
Languageen
Target Filecamera_ready_mlrrn.pdf
User Groupigorcrexito@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 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
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