Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/623941
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dc.contributor.authorAnnan Lien_US
dc.contributor.authorMin Linen_US
dc.contributor.authorYi Wuen_US
dc.contributor.authorMing-Hsuan Yangen_US
dc.contributor.authorShuicheng Yanen_US
dc.date.accessioned2020-05-20T09:27:34Z-
dc.date.available2020-05-20T09:27:34Z-
dc.date.issued2016en_US
dc.identifier.issn0162-8828en_US
dc.identifier.other10.1109/TPAMI.2015.2417577en_US
dc.identifier.urihttp://localhost/handle/Hannan/150979en_US
dc.identifier.urihttp://localhost/handle/Hannan/623941-
dc.descriptionen_US
dc.description.abstractNumerous approaches on object tracking have been proposed during the past decade with demonstrated success. However, most tracking algorithms are evaluated on limited video sequences and annotations. For thorough performance evaluation, we propose a large-scale database which contains 365 challenging image sequences of pedestrians and rigid objects. The database covers 12 kinds of objects, and most of the sequences are captured from moving cameras. Each sequence is annotated with target location and occlusion level for evaluation. A thorough experimental evaluation of 20 state-of-the-art tracking algorithms is presented with detailed analysis using different metrics. The database is publicly available and evaluation can be carried out online for fair assessments of visual tracking algorithms.en_US
dc.publisherIEEEen_US
dc.relation.haspart7072555.pdfen_US
dc.subjectperformance evaluationen_US
dc.subjectObject trackingen_US
dc.subjectbenchmark databaseen_US
dc.titleNUS-PRO: A New Visual Tracking Challengeen_US
dc.typeArticleen_US
dc.journal.volume38en_US
dc.journal.issue2en_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.authorAnnan Lien_US
dc.contributor.authorMin Linen_US
dc.contributor.authorYi Wuen_US
dc.contributor.authorMing-Hsuan Yangen_US
dc.contributor.authorShuicheng Yanen_US
dc.date.accessioned2020-05-20T09:27:34Z-
dc.date.available2020-05-20T09:27:34Z-
dc.date.issued2016en_US
dc.identifier.issn0162-8828en_US
dc.identifier.other10.1109/TPAMI.2015.2417577en_US
dc.identifier.urihttp://localhost/handle/Hannan/150979en_US
dc.identifier.urihttp://localhost/handle/Hannan/623941-
dc.descriptionen_US
dc.description.abstractNumerous approaches on object tracking have been proposed during the past decade with demonstrated success. However, most tracking algorithms are evaluated on limited video sequences and annotations. For thorough performance evaluation, we propose a large-scale database which contains 365 challenging image sequences of pedestrians and rigid objects. The database covers 12 kinds of objects, and most of the sequences are captured from moving cameras. Each sequence is annotated with target location and occlusion level for evaluation. A thorough experimental evaluation of 20 state-of-the-art tracking algorithms is presented with detailed analysis using different metrics. The database is publicly available and evaluation can be carried out online for fair assessments of visual tracking algorithms.en_US
dc.publisherIEEEen_US
dc.relation.haspart7072555.pdfen_US
dc.subjectperformance evaluationen_US
dc.subjectObject trackingen_US
dc.subjectbenchmark databaseen_US
dc.titleNUS-PRO: A New Visual Tracking Challengeen_US
dc.typeArticleen_US
dc.journal.volume38en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7072555.pdf1.44 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAnnan Lien_US
dc.contributor.authorMin Linen_US
dc.contributor.authorYi Wuen_US
dc.contributor.authorMing-Hsuan Yangen_US
dc.contributor.authorShuicheng Yanen_US
dc.date.accessioned2020-05-20T09:27:34Z-
dc.date.available2020-05-20T09:27:34Z-
dc.date.issued2016en_US
dc.identifier.issn0162-8828en_US
dc.identifier.other10.1109/TPAMI.2015.2417577en_US
dc.identifier.urihttp://localhost/handle/Hannan/150979en_US
dc.identifier.urihttp://localhost/handle/Hannan/623941-
dc.descriptionen_US
dc.description.abstractNumerous approaches on object tracking have been proposed during the past decade with demonstrated success. However, most tracking algorithms are evaluated on limited video sequences and annotations. For thorough performance evaluation, we propose a large-scale database which contains 365 challenging image sequences of pedestrians and rigid objects. The database covers 12 kinds of objects, and most of the sequences are captured from moving cameras. Each sequence is annotated with target location and occlusion level for evaluation. A thorough experimental evaluation of 20 state-of-the-art tracking algorithms is presented with detailed analysis using different metrics. The database is publicly available and evaluation can be carried out online for fair assessments of visual tracking algorithms.en_US
dc.publisherIEEEen_US
dc.relation.haspart7072555.pdfen_US
dc.subjectperformance evaluationen_US
dc.subjectObject trackingen_US
dc.subjectbenchmark databaseen_US
dc.titleNUS-PRO: A New Visual Tracking Challengeen_US
dc.typeArticleen_US
dc.journal.volume38en_US
dc.journal.issue2en_US
dc.journal.titleIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7072555.pdf1.44 MBAdobe PDFThumbnail
Preview File