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MARITIME TRAFFIC ANOMALY DETECTION FROM AIS SATELLITE DATA IN NEAR PORT REGIONS

dc.contributor.authorLiu, Bo
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorDr.Evangelos E. Miliosen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerDr.Ronald Peloten_US
dc.contributor.thesis-readerDr.Raza Abidien_US
dc.contributor.thesis-supervisorDr.Stan Matwinen_US
dc.date.accessioned2015-08-14T16:55:26Z
dc.date.available2015-08-14T16:55:26Z
dc.date.defence2015-08-10
dc.date.issued2015
dc.description.abstractMaritime traffic monitoring is an important aspect of safety and security in close-to-port operations. While there is a large amount of data with variable quality, decision makers need reliable information about possible situations or threats. In this thesis, we propose a two-component maritime traffic anomaly detection model. First, it extracts normal ship trajectory patterns using, besides ship tracing data, the publicly available IMO Rules. The main result of clustering is a set of generated lanes that can be mapped to those de ned in the IMO directives. Then, we show how the second anomaly detection component detects anomalous navigational behaviors based on three specialized division distances with the clusters. It decides for each point if the vessel is anomalous, considering longitude, latitude, direction and speed. This point-based approach is applicable for real-time AIS (Automatic Identification System) surveillance; it is also feasible for analysts to set their own threshold for labeling whole trajectories.en_US
dc.identifier.urihttp://hdl.handle.net/10222/60105
dc.language.isoenen_US
dc.subjectmaritime surveilanceen_US
dc.subjecttrajectory miningen_US
dc.subjectclusteringen_US
dc.subjectanomaly detectionen_US
dc.titleMARITIME TRAFFIC ANOMALY DETECTION FROM AIS SATELLITE DATA IN NEAR PORT REGIONSen_US
dc.typeThesisen_US

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