<div class="csl-bib-body">
<div class="csl-entry">Larsen, S. K., Rasmussen, L. E., Jasulaitis, D., Tomer Sagi, Hose, K., & Lehahn, Y. (2024). A benchmark and a multi-stage pipeline for classifying underwater videos at scale. <i>International Journal of Image and Data Fusion</i>. https://doi.org/10.1080/19479832.2024.2416227</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/209499
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dc.description.abstract
Standardised benchmarks have been instrumental in driving the recent progress in computer vision. However, most benchmarks are designed for general-purpose tasks, covering multiple different topics and classes but are limited to the needs of specialised tasks. For example, when performing 3D reconstruction of corals, researchers need to record footage of coral with multiple camera angles. Due to the limited availability of such videos in standard datasets, the ability to reconstruct 3D coral models from public videos would alleviate this problem since it would allow researchers to tap into the vast scope of online content. Thus, one could use machine learning to sift through the immense amounts of content and automatically identify suitable videos for 3D reconstruction. In this work, we introduce a new benchmark that uses amateur footage queried from the YouTube-8 M dataset where each video has been manually labelled for undersea, coral, and multiple camera angles. Furthermore, we construct a three-stage pipeline of machine learning models with the purpose of identifying suitable videos for the 3D reconstruction of coral from the public domain. We instantiate the pipeline with state-of-the-art video classification methods and evaluate their performance on the benchmark, identifying their shortcomings and avenues for future research.
en
dc.description.sponsorship
European Commission
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dc.language.iso
en
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dc.publisher
Taylor & Francis Asia Pacific (Singapore)
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dc.relation.ispartof
International Journal of Image and Data Fusion
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dc.subject
Benchmark
en
dc.subject
underwater
en
dc.subject
coral
en
dc.subject
computer vision
en
dc.subject
underwater video classification
en
dc.subject
underwater object detection
en
dc.subject
deep learning
en
dc.subject
transformers
en
dc.title
A benchmark and a multi-stage pipeline for classifying underwater videos at scale
en
dc.type
Article
en
dc.type
Artikel
de
dc.contributor.affiliation
Aalborg University, Denmark
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dc.contributor.affiliation
Aalborg University, Denmark
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dc.contributor.affiliation
Aalborg University, Denmark
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dc.contributor.affiliation
Aalborg University
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dc.contributor.affiliation
Faculty of Natural Sciences - University of Haifa (Haifa, IL)
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dc.relation.grantno
???
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dc.type.category
Original Research Article
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tuw.journal.peerreviewed
true
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tuw.peerreviewed
true
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wb.publication.intCoWork
International Co-publication
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tuw.project.title
Health virtual twins for the personalised management of stroke related to atrial fibrillation
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tuw.researchTopic.id
I1
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tuw.researchTopic.name
Logic and Computation
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tuw.researchTopic.value
100
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dcterms.isPartOf.title
International Journal of Image and Data Fusion
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tuw.publication.orgunit
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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tuw.publisher.doi
10.1080/19479832.2024.2416227
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dc.date.onlinefirst
2024-12-12
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dc.identifier.eissn
1947-9824
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dc.description.numberOfPages
20
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tuw.author.orcid
0000-0001-7025-8099
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tuw.author.orcid
0000-0003-0852-3407
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wb.sciencebranch
Informatik
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1010
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wb.sciencebranch.value
80
-
wb.sciencebranch.value
20
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item.grantfulltext
none
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item.languageiso639-1
en
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item.openairetype
research article
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item.cerifentitytype
Publications
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item.fulltext
no Fulltext
-
item.openairecristype
http://purl.org/coar/resource_type/c_2df8fbb1
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crisitem.project.funder
European Commission
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crisitem.project.grantno
???
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crisitem.author.dept
Aalborg University
-
crisitem.author.dept
Aalborg University
-
crisitem.author.dept
Aalborg University
-
crisitem.author.dept
Aalborg University
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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crisitem.author.dept
Faculty of Natural Sciences - University of Haifa (Haifa, IL)