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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier6qtX3pFwXQZeBBx/GJNM9
Repositorysid.inpe.br/banon/2005/07.12.19.31
Last Update2005:07.13.03.00.00 (UTC) administrator
Metadatasid.inpe.br/banon/2005/07.12.19.31.20
Metadata Last Update2020:02.19.03.19.12 (UTC) administrator
Citation KeyMoraisCampPáduCarc:2005:PaFiPr
TitleParticle filter-based predictive tracking for robust fish counting
FormatOn-line
Year2005
Access Date2021, Nov. 27
Number of Files1
Size541 KiB
Context area
Author1 Morais, Erikson Freitas de
2 Campos, Mario Fernando Montenegro
3 Pádua, Flávio Luis Cardeal
4 Carceroni, Rodrigo Lima
Affiliation1 Departamento de Ciência da Computação - Universidade Federal de Minas Gerais.
2 Instituto DOCTUM
EditorRodrigues, Maria Andréia Formico
Frery, Alejandro César
e-Mail Addresscardeal@dcc.ufmg.br
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
Conference LocationNatal
Date9-12 Oct. 2005
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-07-17 14:10:59 :: cardeal -> banon ::
2008-08-26 15:17:01 :: banon -> administrator ::
2009-08-13 20:37:48 :: administrator -> banon ::
2010-08-28 20:01:18 :: banon -> administrator ::
2020-02-19 03:19:12 :: administrator -> :: 2005
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordstracking
particle filter
fish counting
BraMBle
AbstractIn this paper we study the use of computer vision techniques for for underwater visual tracking and counting of fishes in vivo. The methodology is based on the application of a Bayesian filtering technique that enables tracking of objects whose number may vary over time. Unlike existing fish-counting methods, this approach provides adequate means for the acquisition of relevant information about characteristics of different fish species such as swimming ability, time of migration and peak flow rates. The system is also able to estimate fish trajectories over time, which can be further used to study their behaviors when swimming in regions of interest. Our experiments demonstrate that the proposed method can operate reliably under severe environmental changes (e.g. variations in water turbidity) and handle problems such as occlusions or large inter-frame motions. The proposed approach was successfully validated with real-world video streams, achieving overall accuracy as high as 81%.
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data URLhttp://urlib.net/ibi/6qtX3pFwXQZeBBx/GJNM9
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZeBBx/GJNM9
Languageen
Target Filepaduaf_fishcounting.pdf
User Groupcardeal
administrator
Visibilityshown
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Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark mirrorrepository nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume

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