Detection of Abnormal Vessel Behaviors from AIS data using GeoTrackNet: from the Laboratory to the Ocean
Résumé
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.
Origine | Fichiers produits par l'(les) auteur(s) |
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