Show simple item record

dc.contributor.authorNordmo, Tor-Arne Schmidt
dc.contributor.authorOvesen, Aril Bernhard
dc.contributor.authorDagenborg, Håvard
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorJohansen, Dag
dc.date.accessioned2023-11-06T11:38:20Z
dc.date.available2023-11-06T11:38:20Z
dc.date.issued2023-08-02
dc.description.abstractDetection of anomalies within data streams is an important task that is useful for different important societal challenges such as in traffic control and fraud detection. To be able to perform anomaly detection, unsupervised analysis of data is an important key factor, especially in domains where obtaining labelled data is difficult or where the anomalies that should be detected are often changing or are not clearly definable at all. In this article, we present a complete machine learning based pipeline for real-time unsupervised anomaly detection that can handle different input data streams simultaneously. We evaluate the usefulness of the proposed method using three wellknown datasets (fall detection, crime detection, and sport event detection) and a completely new and unlabelled dataset within the domain of commercial fishing. For all datasets, our method outperforms the baselines significantly and is able to detect relevant anomalies while simultaneously having low numbers of false positives. In addition to the good detection performance, the presented system can operate in real-time and is also very flexible and easy to expand.en_US
dc.identifier.citationNordmo, Ovesen, Dagenborg, Halvorsen, Riegler, Johansen: Fishing Trawler Event Detection: An Important Step Towards Digitization of Sustainable Fishing. In: Nichele, Aamodt, Misra, Mölder. 2023 3rd International Conference on Applied Artificial Intelligence (ICAPAI), 2023. IEEE conference proceedingsen_US
dc.identifier.cristinIDFRIDAID 2165868
dc.identifier.doi10.1109/ICAPAI58366.2023.10194202
dc.identifier.isbn9798350328929
dc.identifier.urihttps://hdl.handle.net/10037/31676
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.projectIDNorges forskningsråd: 274451en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.titleFishing Trawler Event Detection: An Important Step Towards Digitization of Sustainable Fishingen_US
dc.type.versionacceptedVersionen_US
dc.typeChapteren_US
dc.typeBokkapittelen_US


File(s) in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record