Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/611505
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dc.contributor.authorJu Hong Yoonen_US
dc.contributor.authorMing-Hsuan Yangen_US
dc.contributor.authorKuk-Jin Yoonen_US
dc.date.accessioned2020-05-20T09:09:35Z-
dc.date.available2020-05-20T09:09:35Z-
dc.date.issued2016en_US
dc.identifier.issn0162-8828en_US
dc.identifier.other10.1109/TPAMI.2015.2473862en_US
dc.identifier.urihttp://localhost/handle/Hannan/142762en_US
dc.identifier.urihttp://localhost/handle/Hannan/611505-
dc.descriptionen_US
dc.description.abstractA robust algorithm is proposed for tracking a target object in dynamic conditions including motion blurs, illumination changes, pose variations, and occlusions. To cope with these challenging factors, multiple trackers based on different feature representations are integrated within a probabilistic framework. Each view of the proposed multiview (multi-channel) feature learning algorithm is concerned with one particular feature representation of a target object from which a tracker is developed with different levels of reliability. With the multiple trackers, the proposed algorithm exploits tracker interaction and selection for robust tracking performance. In the tracker interaction, a transition probability matrix is used to estimate dependencies between trackers. Multiple trackers communicate with each other by sharing information of sample distributions. The tracker selection process determines the most reliable tracker with the highest probability. To account for object appearance changes, the transition probability matrix and tracker probability are updated in a recursive Bayesian framework by reflecting the tracker reliability measured by a robust tracker likelihood function that learns to account for both transient and stable appearance changes. Experimental results on benchmark datasets demonstrate that the proposed interacting multiview algorithm performs robustly and favorably against state-of-the-art methods in terms of several quantitative metrics.en_US
dc.publisherIEEEen_US
dc.relation.haspart7226831.pdfen_US
dc.subjectmultiview representations|tracker interaction|multiple features|Object tracking|transition probability matrixen_US
dc.titleInteracting Multiview Trackeren_US
dc.typeArticleen_US
dc.journal.volume38en_US
dc.journal.issue5en_US
dc.journal.titleIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorJu Hong Yoonen_US
dc.contributor.authorMing-Hsuan Yangen_US
dc.contributor.authorKuk-Jin Yoonen_US
dc.date.accessioned2020-05-20T09:09:35Z-
dc.date.available2020-05-20T09:09:35Z-
dc.date.issued2016en_US
dc.identifier.issn0162-8828en_US
dc.identifier.other10.1109/TPAMI.2015.2473862en_US
dc.identifier.urihttp://localhost/handle/Hannan/142762en_US
dc.identifier.urihttp://localhost/handle/Hannan/611505-
dc.descriptionen_US
dc.description.abstractA robust algorithm is proposed for tracking a target object in dynamic conditions including motion blurs, illumination changes, pose variations, and occlusions. To cope with these challenging factors, multiple trackers based on different feature representations are integrated within a probabilistic framework. Each view of the proposed multiview (multi-channel) feature learning algorithm is concerned with one particular feature representation of a target object from which a tracker is developed with different levels of reliability. With the multiple trackers, the proposed algorithm exploits tracker interaction and selection for robust tracking performance. In the tracker interaction, a transition probability matrix is used to estimate dependencies between trackers. Multiple trackers communicate with each other by sharing information of sample distributions. The tracker selection process determines the most reliable tracker with the highest probability. To account for object appearance changes, the transition probability matrix and tracker probability are updated in a recursive Bayesian framework by reflecting the tracker reliability measured by a robust tracker likelihood function that learns to account for both transient and stable appearance changes. Experimental results on benchmark datasets demonstrate that the proposed interacting multiview algorithm performs robustly and favorably against state-of-the-art methods in terms of several quantitative metrics.en_US
dc.publisherIEEEen_US
dc.relation.haspart7226831.pdfen_US
dc.subjectmultiview representations|tracker interaction|multiple features|Object tracking|transition probability matrixen_US
dc.titleInteracting Multiview Trackeren_US
dc.typeArticleen_US
dc.journal.volume38en_US
dc.journal.issue5en_US
dc.journal.titleIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7226831.pdf10.96 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJu Hong Yoonen_US
dc.contributor.authorMing-Hsuan Yangen_US
dc.contributor.authorKuk-Jin Yoonen_US
dc.date.accessioned2020-05-20T09:09:35Z-
dc.date.available2020-05-20T09:09:35Z-
dc.date.issued2016en_US
dc.identifier.issn0162-8828en_US
dc.identifier.other10.1109/TPAMI.2015.2473862en_US
dc.identifier.urihttp://localhost/handle/Hannan/142762en_US
dc.identifier.urihttp://localhost/handle/Hannan/611505-
dc.descriptionen_US
dc.description.abstractA robust algorithm is proposed for tracking a target object in dynamic conditions including motion blurs, illumination changes, pose variations, and occlusions. To cope with these challenging factors, multiple trackers based on different feature representations are integrated within a probabilistic framework. Each view of the proposed multiview (multi-channel) feature learning algorithm is concerned with one particular feature representation of a target object from which a tracker is developed with different levels of reliability. With the multiple trackers, the proposed algorithm exploits tracker interaction and selection for robust tracking performance. In the tracker interaction, a transition probability matrix is used to estimate dependencies between trackers. Multiple trackers communicate with each other by sharing information of sample distributions. The tracker selection process determines the most reliable tracker with the highest probability. To account for object appearance changes, the transition probability matrix and tracker probability are updated in a recursive Bayesian framework by reflecting the tracker reliability measured by a robust tracker likelihood function that learns to account for both transient and stable appearance changes. Experimental results on benchmark datasets demonstrate that the proposed interacting multiview algorithm performs robustly and favorably against state-of-the-art methods in terms of several quantitative metrics.en_US
dc.publisherIEEEen_US
dc.relation.haspart7226831.pdfen_US
dc.subjectmultiview representations|tracker interaction|multiple features|Object tracking|transition probability matrixen_US
dc.titleInteracting Multiview Trackeren_US
dc.typeArticleen_US
dc.journal.volume38en_US
dc.journal.issue5en_US
dc.journal.titleIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7226831.pdf10.96 MBAdobe PDFThumbnail
Preview File