A Contextually Supported Abnormality Detector for Maritime Trajectories

Kristoffer Vinther Olesen*, Ahcène Boubekki, Michael C. Kampffmeyer, Robert Jenssen, Anders Nymark Christensen, Sune Hørlück, Line H. Clemmensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

21 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number2085
JournalJournal of Marine Science and Engineering
Volume11
Issue number11
Number of pages27
ISSN2077-1312
DOIs
Publication statusPublished - 2023

Keywords

  • AIS
  • Anomaly detection
  • Maritime surveillance
  • Maritime traffic patterns
  • Trajectory clustering
  • Vessel traffic service

Fingerprint

Dive into the research topics of 'A Contextually Supported Abnormality Detector for Maritime Trajectories'. Together they form a unique fingerprint.

Cite this