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Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12109)

Abstract

The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade. Given the enormous volume of vessel data continuously being generated, real-time analysis of vessel behaviors is only possible because of decision support systems provided with event and anomaly detection methods. However, current works on vessel event detection are ad-hoc methods able to handle only a single or a few predefined types of vessel behavior. Most of the existing approaches do not learn from the data and require the definition of queries and rules for describing each behavior. In this paper, we discuss challenges and opportunities in classical machine learning and deep learning for vessel event and anomaly detection.

Keywords

This study was financed in part by the Brazilian agencies CNPq and CAPES - Project Big Data Analytics: Lançando Luz do Genes ao Cosmos (CAPES/PRINT process number 88887.310782/2018-00), Fundação de Amparo a Pesquisa e Inovação do Estado de Santa Catarina - Project Match (co-financing of H2020 Projects - Grant 2018TR 1266), and the European Union’s Horizon 2020 research and innovation programme under Grant Agreement 777695 (MASTER). The authors also acknowledge the support of the Natural Sciences and Engineering Research Council of Canada and of Global Affairs Canada - ELAP for this research.

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Correspondence to Lucas May Petry .

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May Petry, L., Soares, A., Bogorny, V., Brandoli, B., Matwin, S. (2020). Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_41

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  • DOI: https://doi.org/10.1007/978-3-030-47358-7_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-47357-0

  • Online ISBN: 978-3-030-47358-7

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