Techniques for anomaly detection in the maritime domain by extracting traffic patterns from ship position data to generate atlases of expected ocean travel are developed in this thesis. An archive of historical data is used to develop a traffic density grid. The Hough transformation is used to extract linear patterns of elevated density from the traffic density grid, which can be considered the highways of the oceans. These highways collectively create an atlas that is used to define geographical regions of expected ship locations. Ship position reports are compared to the atlas of highways to flag as anomalous any ship that is not operating on an expected highway. The atlas generation techniques are demonstrated using automated information system (AIS) ship position data to detect highways in both open-ocean and coastal areas. Additionally, the atlas generation techniques are used to explore variability in ship traffic as a result of extreme weather and seasonal variation. Finally, anomaly detection is demonstrated by comparing AIS data from 2013 to the highways detected in the archive of data from 2012. The development of an automatic atlas generation technique that can be used to develop a definition of normal maritime behavior is the significant result of this thesis.
Traffic Pattern Detection Using the Hough Transformation for Anomaly Detection to Improve Maritime Domain Awareness
2013
105 pages
Report
Keine Angabe
Englisch
Marine Engineering , Navigation Systems , Optical Detection , Anomalies , Detection , Water traffic , Archives , Automatic , Automation , Coastal regions , Density , Geographic areas , Grids , Highways , Information systems , Linearity , Oceans , Patterns , Position(location) , Seasonal variations , Ships , Theses , Traffic , Travel , Weather , Maritime domain awareness , Hough transformation , Anomaly detection , Automated information system , Pattern extraction
Unsupervised Trajectory Anomaly Detection for Situation Awareness in Maritime Navigation
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