Abstract
In this work we describe our first steps towards our H2020 project MARISA participation, where we intent to develop a tool-kit towards the identification of outliers in Vessel trajectories based on electronic data regarding position and time. These outliers can correspond to illegal activities that could be related with illegal immigration, drugs transshipment among others. We developed process tools that based on any electronic Vessel position systems, like Automatic Identification System (AIS) data, it is possible to extract routes in an unsupervised approach. At the same time identify non-conformities based on AIS data signal lost and to identify situation when two or more Vessels are approaching close to each other, called the rendezvous.
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Notes
- 1.
ICS Shipping and World Trade, www.ics-shipping.org/shipping-facts.
- 2.
Marisa Project - www.marisaproject.eu.
- 3.
QGIS Geographic Information System, http://qgis.osgeo.org.
- 4.
Solas Chapter V Annex 17 AIS - www.mcanet.mcga.gov.uk.
- 5.
Vessel Tracking Data, www.amsa.gov.au/Spatial/DataServices/DigitalData.
- 6.
MarineCadastre Vessel Traffic Data, www.marinecadastre.gov/ais.
References
Lee, J.-G., et al.: Trajectory outlier detection: a partition-and-detect framework. In: 2008 IEEE 24th International Conference on Data Engineering (2008)
Lane, R.O., et al. Maritime anomaly detection and threat assessment. In: 2010 13th International Conference on Information Fusion (2010)
Lei, P.-R.: A framework for anomaly detection in maritime trajectory behavior. Knowl. Inf. Syst. 47(1), 189–214 (2015)
Pallotta, G., et al.: Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction. Entropy 15(12) (2013)
Shahir, H.Y., et al.: Maritime situation analysis: a multi-vessel interaction and anomaly detection framework. In: 2014 IEEE Joint Intelligence and Security Informatics Conference (2014)
Laxhammar, R.: Anomaly detection for sea surveillance. In: 2008 11th International Conference on Information Fusion (2008)
Mao, S., et al.: An automatic identification system (AIS) database for maritime trajectory prediction and data mining. In: Proceedings in Adaptation, Learning and Optimization, Proceedings of ELM-2016 (2017)
Le Guillarme, N., Lerouvreur, X.: Unsupervised extraction of knowledge from S-AIS data for maritime situational awareness. In: Proceedings of the 16th International Conference on Information Fusion (2013)
Morris, B.T., Trivedi, M.M.: A survey of vision-based trajectory learning and analysis for surveillance. IEEE Trans. Circuits Syst. Video Technol. 18(8), 1114 (2008)
Natale, F., et al.: Mapping fishing effort through AIS data. Plos One 10(6) 2015
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Machado, T., Maia, R., Santos, P., Ferreira, J. (2019). Vessel Trajectories Outliers. In: Novais, P., et al. Ambient Intelligence – Software and Applications –, 9th International Symposium on Ambient Intelligence. ISAmI2018 2018. Advances in Intelligent Systems and Computing, vol 806. Springer, Cham. https://doi.org/10.1007/978-3-030-01746-0_29
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DOI: https://doi.org/10.1007/978-3-030-01746-0_29
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