dc.contributor.author | Pallotta, Giuliana | |
dc.contributor.author | Jousselme, Anne-Laure | |
dc.date.accessioned | 2019-06-20T08:36:12Z | |
dc.date.available | 2019-06-20T08:36:12Z | |
dc.date.issued | 2019/06 | |
dc.identifier.govdoc | CMRE-PR-2019-108 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12489/848 | |
dc.description.abstract | Discovering anomalies at sea is one of the critical tasks of Maritime Situational Awareness (MSA) activities and an important enabler for maritime security operations. This paper proposes a data-driven approach to anomaly detection, highlighting challenges specific to the maritime domain. This work builds on unsupervised learning techniques which provide models for normal traffic behaviour. A methodology to associate tracks to the derived traffic model is then presented. This is done by the pre-extraction of contextual information as the baseline patterns of life (i.e., routes) in the area under investigation. In addition to a brief description of the approach to derive the routes, their characterization and representation is presented in support of exploitable knowledge to classify anomalies. A hierarchical reasoning is proposed where new tracks are first associated to existing routes based on their positional information only and "off-route" vessels are detected. Then, for on-route vessels further anomalies are detected such as "speed anomaly" or "heading anomaly". The algorithm is illustrated and assessed on a real-world dataset supplemented with synthetic abnormal tracks. | en_US |
dc.format | 8 p. : ill. ; digital, PDF file | en_US |
dc.language.iso | en | en_US |
dc.publisher | CMRE | en_US |
dc.source | In: 18th International Conference on Information Fusion, 6-9 July 2015, Washington DC, USA, pp. 1152-1159 | en_US |
dc.subject | Maritime situational awareness | en_US |
dc.subject | Maritime surveillance | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Ship routing | en_US |
dc.subject | Maritime route extraction | en_US |
dc.subject | Maritime route prediction | en_US |
dc.subject | Ship tracking | en_US |
dc.subject | Radar | en_US |
dc.subject | Data mining | en_US |
dc.subject | Target speed | en_US |
dc.subject | Target bearing | en_US |
dc.title | Data-driven detection and context-based classification of maritime anomalies | en_US |
dc.type | Reprint (PR) | en_US |
dc.type | Papers and Articles | en_US |