@misc{16755,
  author = {Tor-Arne Nordmo and Aril Ovesen and Bj{\o}rn Juliussen and Steven Hicks and Vajira Thambawita and H{\r a}vard Johansen and P{\r a}l Halvorsen and Michael Riegler and Dag Johansen},
  editor = {Niall Murray and Gwendal Simon and Mylene Farias and Irene Viola and Mario Montagud},
  title = {Njord: a fishing trawler dataset},
  abstract = {Fish 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.},
  year = {2022},
  journal = {Proceedings of the 13th ACM Multimedia Systems Conference (MMSYS)},
  month = {08/2022},
  publisher = {ACM},
  address = {New York, NY, USA},
  isbn = {9781450392839},
  url = {https://dl.acm.org/doi/pdf/10.1145/3524273.3532886},
  doi = {10.1145/3524273.3532886},
}