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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier6qtX3pFwXQZG2LgkFdY/LNm7D
Repositorysid.inpe.br/sibgrapi@80/2006/07.19.03.17
Last Update2006:07.19.03.17.46 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2006/07.19.03.17.47
Metadata Last Update2022:06.14.00.13.15 (UTC) administrator
DOI10.1109/SIBGRAPI.2006.42
Citation KeyBrandãoWainGold:2006:SuHiPa
TitleSubspace Hierarchical Particle Filter
FormatOn-line
Year2006
Access Date2024, May 02
Number of Files1
Size249 KiB
2. Context
Author1 Brandão, Bruno Cedraz
2 Wainer, Jacques
3 Goldenstein, Siome Klein
Affiliation1 UNICAMP
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
e-Mail Addressbrunocedraz@gmail.com
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus, AM, Brazil
Date8-11 Oct. 2006
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-07-17 14:11:03 :: brunocedraz -> administrator ::
2009-08-13 20:38:08 :: administrator -> banon ::
2010-08-28 20:02:24 :: banon -> administrator ::
2022-06-14 00:13:15 :: administrator -> :: 2006
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordstracking of objects
humans
articulated structures
particle filtering
AbstractParticle filtering has become a standard tool for non-parametric estimation in computer vision tracking applications. It is an instance of stochastic search. Each particle represents a possible state of the system. Higher concentration of particles at any given region of the search space implies higher probabilities. One of its major drawbacks is the exponential growth in the number of particles for increasing dimensions in the search space. We present a graph based filtering framework for hierarchical model tracking that is capable of substantially alleviate this issue. The method relies on dividing the search space in subspaces that can be estimated separately. Low correlated subspaces may be estimated with parallel, or serial, filters and have their probability distributions combined by a special aggregator filter. We describe a new algorithm to extract parameter groups, which define the subspaces, from the system model. We validate our method with different graph structures withing a simple hand tracking experiment with both synthetic and real data.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2006 > Subspace Hierarchical Particle...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Subspace Hierarchical Particle...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/LNm7D
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZG2LgkFdY/LNm7D
Languageen
Target Filebrandao-SHPF.pdf
User Groupbrunocedraz
administrator
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46RFT7E
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.08.00.20 4
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 mirrorrepository 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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