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Reference TypeConference Paper (Conference Proceedings)
Last Update2005: (UTC) administrator
Metadata Last Update2020: (UTC) administrator
Citation KeyMoraisCampPáduCarc:2005:PaFiPr
TitleParticle filter-based predictive tracking for robust fish counting
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
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
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
particle filter
fish counting
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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Target Filepaduaf_fishcounting.pdf
User Groupcardeal
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