Symbiotic Black-Box Tracker
Identifieur interne : 001A11 ( Istex/Corpus ); précédent : 001A10; suivant : 001A12Symbiotic Black-Box Tracker
Auteurs : Longfei Zhang ; Yue Gao ; Alexander Hauptmann ; Rongrong Ji ; Gangyi Ding ; Boaz SuperSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2012.
English descriptors
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Abstract
Abstract: Many trackers have been proposed for tracking objects individually in previous research. However, it is still difficult to trust any single tracker over a variety of circumstances. Therefore, it is important to estimate how well each tracker performs and fusion the tracking results. In this paper, we propose a symbiotic black-box tracker (SBB) that learns only from the output of individual trackers, which run in parallel, without any detailed information about these trackers and selects the best one to generate the tracking result. All trackers are considered as black-boxes and SBB learns the best combination scheme for all existing tracking results. SBB estimates confidence scores of these trackers. The confidence score is estimated based on the tracking performance of each tracker and the consistency performance among different trackers. SBB is employed to select the best tracker with the maximum confidence score. Experiments and comparisons conducted on the “Caremedia” dataset and the “Caviar” dataset demonstrate the effectiveness of the proposed method.
Url:
DOI: 10.1007/978-3-642-27355-1_14
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<surname>Vardi</surname>
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<abstract xml:lang="en"><p>Abstract: Many trackers have been proposed for tracking objects individually in previous research. However, it is still difficult to trust any single tracker over a variety of circumstances. Therefore, it is important to estimate how well each tracker performs and fusion the tracking results. In this paper, we propose a symbiotic black-box tracker (SBB) that learns only from the output of individual trackers, which run in parallel, without any detailed information about these trackers and selects the best one to generate the tracking result. All trackers are considered as black-boxes and SBB learns the best combination scheme for all existing tracking results. SBB estimates confidence scores of these trackers. The confidence score is estimated based on the tracking performance of each tracker and the consistency performance among different trackers. SBB is employed to select the best tracker with the maximum confidence score. Experiments and comparisons conducted on the “Caremedia” dataset and the “Caviar” dataset demonstrate the effectiveness of the proposed method.</p>
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<Author AffiliationIDS="Aff25"><AuthorName DisplayOrder="Western"><GivenName>Rongrong</GivenName>
<FamilyName>Ji</FamilyName>
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<FamilyName>Ding</FamilyName>
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<Author AffiliationIDS="Aff26"><AuthorName DisplayOrder="Western"><GivenName>Boaz</GivenName>
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<Affiliation ID="Aff22"><OrgDivision>School of Software</OrgDivision>
<OrgName>Beijing Institute of Technology</OrgName>
<OrgAddress><City>Beijing</City>
<Country>China</Country>
</OrgAddress>
</Affiliation>
<Affiliation ID="Aff23"><OrgDivision>School of Computer Science</OrgDivision>
<OrgName>Carnegie Mellon University</OrgName>
<OrgAddress><City>Pittsburgh</City>
<State>PA</State>
<Country>USA</Country>
</OrgAddress>
</Affiliation>
<Affiliation ID="Aff24"><OrgDivision>Department of Automation</OrgDivision>
<OrgName>Tsinghua University</OrgName>
<OrgAddress><City>Beijing</City>
<Country>China</Country>
</OrgAddress>
</Affiliation>
<Affiliation ID="Aff25"><OrgDivision>Department of Electronic Engineering</OrgDivision>
<OrgName>Columbia University</OrgName>
<OrgAddress><City>New York</City>
<State>NY</State>
<Country>USA</Country>
</OrgAddress>
</Affiliation>
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<State>IL</State>
<Country>USA</Country>
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<Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading>
<Para>Many trackers have been proposed for tracking objects individually in previous research. However, it is still difficult to trust any single tracker over a variety of circumstances. Therefore, it is important to estimate how well each tracker performs and fusion the tracking results. In this paper, we propose a symbiotic black-box tracker (SBB) that learns only from the output of individual trackers, which run in parallel, without any detailed information about these trackers and selects the best one to generate the tracking result. All trackers are considered as black-boxes and SBB learns the best combination scheme for all existing tracking results. SBB estimates confidence scores of these trackers. The confidence score is estimated based on the tracking performance of each tracker and the consistency performance among different trackers. SBB is employed to select the best tracker with the maximum confidence score. Experiments and comparisons conducted on the “Caremedia” dataset and the “Caviar” dataset demonstrate the effectiveness of the proposed method.</Para>
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<KeywordGroup Language="En"><Heading>Keywords</Heading>
<Keyword>Object tracking</Keyword>
<Keyword>information propagation</Keyword>
<Keyword>data association</Keyword>
<Keyword>multi-tracker</Keyword>
</KeywordGroup>
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<name type="personal"><namePart type="given">Longfei</namePart>
<namePart type="family">Zhang</namePart>
<affiliation>School of Software, Beijing Institute of Technology, Beijing, China</affiliation>
<affiliation>School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA</affiliation>
<affiliation>E-mail: longfeizhang@cs.cmu.edu</affiliation>
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<name type="personal"><namePart type="given">Yue</namePart>
<namePart type="family">Gao</namePart>
<affiliation>School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA</affiliation>
<affiliation>Department of Automation, Tsinghua University, Beijing, China</affiliation>
<affiliation>E-mail: kevin.gaoy@gmail.com</affiliation>
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<name type="personal"><namePart type="given">Alexander</namePart>
<namePart type="family">Hauptmann</namePart>
<affiliation>School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA</affiliation>
<affiliation>E-mail: alex@cs.cmu.edu</affiliation>
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<name type="personal"><namePart type="given">Rongrong</namePart>
<namePart type="family">Ji</namePart>
<affiliation>Department of Electronic Engineering, Columbia University, New York, NY, USA</affiliation>
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<name type="personal"><namePart type="given">Gangyi</namePart>
<namePart type="family">Ding</namePart>
<affiliation>School of Software, Beijing Institute of Technology, Beijing, China</affiliation>
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<name type="personal"><namePart type="given">Boaz</namePart>
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<abstract lang="en">Abstract: Many trackers have been proposed for tracking objects individually in previous research. However, it is still difficult to trust any single tracker over a variety of circumstances. Therefore, it is important to estimate how well each tracker performs and fusion the tracking results. In this paper, we propose a symbiotic black-box tracker (SBB) that learns only from the output of individual trackers, which run in parallel, without any detailed information about these trackers and selects the best one to generate the tracking result. All trackers are considered as black-boxes and SBB learns the best combination scheme for all existing tracking results. SBB estimates confidence scores of these trackers. The confidence score is estimated based on the tracking performance of each tracker and the consistency performance among different trackers. SBB is employed to select the best tracker with the maximum confidence score. Experiments and comparisons conducted on the “Caremedia” dataset and the “Caviar” dataset demonstrate the effectiveness of the proposed method.</abstract>
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