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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPAW/3PFR8KB
Repositorysid.inpe.br/sibgrapi/2017/08.21.20.57
Last Update2017:08.21.20.57.03 administrator
Metadatasid.inpe.br/sibgrapi/2017/08.21.20.57.03
Metadata Last Update2020:02.19.02.01.32 administrator
Citation KeyQuiritaHappCostFeit:2017:SyTrEn
TitleSymbiotic tracker ensemble with feedback learning
FormatOn-line
Year2017
Access Date2021, Jan. 25
Number of Files1
Size2945 KiB
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Author1 Quirita, Victor Hugo Ayma
2 Happ, Patrick Nigri
3 Costa, Gilson Alexandre Ostwald Pedro da
4 Feitosa, Raul Queiroz
Affiliation1 ELECTRICAL ENGINEERING DEPARTMENT, PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO
2 ELECTRICAL ENGINEERING DEPARTMENT, PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO
3 INFORMATICS AND COMPUTER SCIENCE DEPARTMENT, STATE UNIVERSITY OF RIO DE JANEIRO
4 ELECTRICAL ENGINEERING DEPARTMENT, PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressvhaymaq@ele.puc-rio.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-21 20:57:03 :: vhaymaq@ele.puc-rio.br -> administrator ::
2020-02-19 02:01:32 :: administrator -> :: 2017
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsOBJECT TRACKING, TRACKING FUSION.
AbstractVisual tracking is a challenging task due to a number of factors, such as occlusions, deformations, illumination variations and abrupt motion changes present in a video sequence. Generally, trackers are robust to some of these factors, but do not achieve satisfactory results when dealing with multiple factors at the same time. More robust results when multiple factors are present can be obtained by combining the results of different trackers. In this paper we propose a multiple tracker fusion method, named Symbiotic Tracker Ensemble with Feedback Learning (SymTE-FL), which combines the results of a set of trackers to produce a unified tracking estimate. The novelty of the method consists in providing feedback to the individual trackers, so that they can correct their own estimates, thus improving overall tracking accuracy. The proposal is validated by experiments conducted upon a publicly available database. The results show that the proposed method delivered in average more accurate tracking estimates than those obtained with individual trackers running independently and with the original approach.
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data URLhttp://urlib.net/rep/8JMKD3MGPAW/3PFR8KB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFR8KB
Languageen
Target File2017_SIBGRAPI_VHAQ.pdf
User Groupvhaymaq@ele.puc-rio.br
Visibilityshown
Update Permissionnot transferred
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PJT9LS
8JMKD3MGPAW/3PKCC58
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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