A Contextually Supported Abnormality Detector for Maritime Trajectories
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The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal maritime behavior. Current models lack interpretability and contextualization of their predictions and are generally not quantitatively evaluated on a large annotated dataset comprising all expected traffic in a Region of Interest. We propose a model for the detection of abnormal maritime behaviors that provides the closest behaviors as context to the predictions. The normalcy model relies on two-step clustering, which is first computed based on the positions of the vessels and then refined based on their kinematics. We design for each step a similarity measure, which combined are able to distinguish boats cruising shipping lanes in different directions, but also vessels with more freedom, such as pilot boats. Our proposed abnormality detection model achieved, on a large annotated dataset extracted from AIS logs that we publish, an ROC-AUC of 0.79, which is on a par with State-of-the-Art deep neural networks, while being more computationally efficient and more interpretable, thanks to the contextualization offered by our two-step clustering.
Originalsprog | Engelsk |
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Artikelnummer | 2085 |
Tidsskrift | Journal of Marine Science and Engineering |
Vol/bind | 11 |
Udgave nummer | 11 |
Antal sider | 27 |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
This work was financially supported by the Danish Ministry of Defence Acquisition and Logistics Organisation, grant no. 4600005159. Visual Intelligence publications are financially supported by the Research Council of Norway, through its Centre for Research-based Innovation funding scheme (grant no. 309439), and Consortium Partners.
Publisher Copyright:
© 2023 by the authors.
ID: 391000282