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Data-driven detection and context-based classification of maritime anomalies

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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.

URI
http://hdl.handle.net/20.500.12489/848

Report Number
CMRE-PR-2019-108

Source
In: 18th International Conference on Information Fusion, 6-9 July 2015, Washington DC, USA, pp. 1152-1159

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Date
2019/06

Author(s)
Pallotta, Giuliana
; 
Jousselme, Anne-Laure

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CMRE-PR-2019-108.pdf (2.938Mb)

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