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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3C8G5FH
Repositorysid.inpe.br/sibgrapi/2012/07.06.21.52
Last Update2012:07.06.21.52.50 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2012/07.06.21.52.50
Metadata Last Update2022:06.14.00.07.24 (UTC) administrator
DOI10.1109/SIBGRAPI.2012.44
Citation KeyMirandaViMaLeViCa:2012:ReGeRe
TitleReal-time gesture recognition from depth data through key poses learning and decision forests
FormatDVD, On-line.
Year2012
Access Date2024, May 02
Number of Files1
Size1840 KiB
2. Context
Author1 Miranda, Leandro
2 Vieira, Thales
3 Martinez, Dimas
4 Lewiner, Thomas
5 Vieira, Antônio W.
6 Campos, Mario F. M.
Affiliation1 Mathematics, UFAL 
2 Mathematics, UFAL 
3 Mathematics, UFAL 
4 Mathematics, PUC-Rio 
5 Computer Science, UFMG 
6 Computer Science, UFMG
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
e-Mail Addressthalesv@gmail.com
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto, MG, Brazil
Date22-25 Aug. 2012
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2012-09-20 16:45:34 :: thalesv@gmail.com -> administrator :: 2012
2022-03-08 21:03:22 :: administrator -> menottid@gmail.com :: 2012
2022-03-10 13:01:15 :: menottid@gmail.com -> administrator :: 2012
2022-06-14 00:07:24 :: administrator -> :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsGesture recognition
Pose identification
Depth sensors
3d motion
Natural user interface
AbstractHuman gesture recognition is a challenging task with many applications. The popularization of real time depth sensors even diversifies potential applications to end-user natural user interface (NUI). The quality of such NUI highly depends on the robustness and execution speed of the gesture recognition. This work introduces a method for real-time gesture recognition from a noisy skeleton stream, such as the ones extracted from Kinect depth sensors. Each pose is described using a tailored angular representation of the skeleton joints. Those descriptors serve to identify key poses through a multi-class classifier derived from Support Vector learning machines. The gesture is labeled on-the-fly from the key pose sequence through a decision forest, that naturally performs the gesture time warping and avoids the requirement for an initial or neutral pose. The proposed method runs in real time and shows robustness in several experiments.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2012 > Real-time gesture recognition...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Real-time gesture recognition...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3C8G5FH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3C8G5FH
Languageen
Target Filegesture_learning_sibgrapi_certified.pdf
User Groupthalesv@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SL8GS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.03.31 5
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


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