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dc.contributor.authorNordmo, Tor-Arne Schmidt
dc.contributor.authorOvesen, Aril Bernhard
dc.contributor.authorJuliussen, Bjørn Aslak
dc.contributor.authorHicks, Steven
dc.contributor.authorThambawita, Vajira L B
dc.contributor.authorJohansen, Håvard D.
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorJohansen, Dag
dc.description.abstractFish is one of the main sources of food worldwide. The commercial fishing industry has a lot of different aspects to consider, ranging from sustainability to reporting. The complexity of the domain also attracts a lot of research from different fields like marine biology, fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote sensing and classification of fish from images or videos using machine learning or other analysis methods attracts growing attention. Surprisingly, little work has been done that considers what is happening on board the fishing vessels. On the deck of the boats, a lot of data and important information are generated with potential applications, such as automatic detection of accidents or automatic reporting of fish caught. This paper presents Njord, a fishing trawler dataset consisting of surveillance videos from a modern off-shore fishing trawler at sea. The main goal of this dataset is to show the potential and possibilities that analysis of such data can provide. In addition to the data, we provide a baseline analysis and discuss several possible research questions this dataset could help answer.en_US
dc.relation.ispartofMMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleNjord: a fishing trawler dataseten_US
dc.relation.projectNorges forskningsråd: 274451en_US

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal