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Detection of Abnormal Vessel Behaviors from AIS data using GeoTrackNet: from the Laboratory to the Ocean

Abstract : The constant growth of maritime traffic leads to the need of automatic anomaly detection, which has been attracting great research attention. Information provided by AIS (Auto-matic Identification System) data, together with recent outstanding progresses of deep learning, make vessel monitoring using neural networks (NNs) a very promising approach. The global of this paper is to further analyse a novel neural network we have recently introduced-GeoTrackNet-regarding operational contexts. Especially, we aim to evaluate (i) the relevance of the abnormal behaviours detected by GeoTrackNet with respect to expert interpretations, (ii) the extent to which GeoTrackNet may process AIS data streams in real time. We report experiments showing the high potential to meet operational level of the model.
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https://hal.archives-ouvertes.fr/hal-02523279
Contributor : Duong Nguyen <>
Submitted on : Wednesday, August 12, 2020 - 7:15:33 PM
Last modification on : Tuesday, March 30, 2021 - 12:26:05 PM

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Duong Nguyen, Matthieu Simonin, Guillaume Hajduch, Rodolphe Vadaine, Cédric Tedeschi, et al.. Detection of Abnormal Vessel Behaviors from AIS data using GeoTrackNet: from the Laboratory to the Ocean. MBDW 2020 : 2nd Maritime Big Data Workshop part of MDM 2020 : 21st IEEE International Conference on Mobile Data Management, Jun 2020, Versailles, France. pp.264-268, ⟨10.1109/MDM48529.2020.00061⟩. ⟨hal-02523279v2⟩

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