Author Identifier
Travis Rybicki: https://orcid.org/0000-0002-5661-2757
Martin Masek: https://orcid.org/0000-0001-8620-6779
Chiou Peng Lam: https://orcid.org/0000-0002-4843-9229
Document Type
Conference Proceeding
Publication Title
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume
10
Issue
4
First Page
295
Last Page
301
Publisher
International Society of Photogrammetry and Remote Sensing
School
School of Science
RAS ID
76556
Funders
Commonwealth of Australia as represented by the Defence Science and Technology Group (DSTG) of the Department of Defence
Abstract
Monitoring maritime traffic has become an important task for ensuring the safety of vessels, as well as the goods, and persons that they may be transporting. An active area of research is the modelling of expected normal vessel behaviour so as to detect subsequent anomalies in new data. Anomalies indicate that a vessel is not behaving in an expected manner and their detection can be flagged for further investigation to identify whether the vessel needs assistance or intervention. An important factor for some vessels in determining normal behaviour is seasonal context. However, current approaches typically do not incorporate seasonality into the model. In this paper, an approach is presented where seasonal context is incorporated into the behaviour model. Seasonal context is first incorporated into vessel trajectory data by encoding the month of year into a historic dataset. Following this, a model of normal behaviour is generated using a clustering approach, with DBSCAN used in this paper. Details of setting the DBSCAN parameters appropriately for vessel trajectory data are provided and four distance metrics explored. Resulting cluster models are evaluated in the context of using the model to classify previously unseen data as either fitting the model or constituting an anomaly. The experiments focus on using fishing vessels within two identified seasons to build the normal model, which is evaluated with a mixture of in season and out of season fishing and non-fishing vessel behaviour.
DOI
10.5194/isprs-annals-X-4-2024-295-2024
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Rybicki, T., Masek, M., & Lam, C. P. (2024). Maritime behaviour anomaly detection with seasonal context. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 295-301. https://doi.org/10.5194/isprs-annals-X-4-2024-295-2024