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GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection

Abstract : Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach---referred to as GeoTrackNet---for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks and a contrario detection to detect abnormal events. The neural network provides a new means to capture complex and heterogeneous patterns in vessels' behaviours, while the \textit{a contrario} detector takes into account the fact that the learnt distribution may be location-dependent. Experiments on a real AIS dataset comprising more than 4.2 million AIS messages demonstrate the relevance of the proposed method compared with state-of-the-art schemes.
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https://hal.archives-ouvertes.fr/hal-02388260
Contributor : Duong Nguyen <>
Submitted on : Monday, February 8, 2021 - 10:08:06 PM
Last modification on : Saturday, February 20, 2021 - 3:14:17 AM

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Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, René Garello, Ronan Fablet. GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection. IEEE Transactions on Intelligent Transportation Systems, IEEE, 2021, ⟨10.1109/TITS.2021.3055614⟩. ⟨hal-02388260v4⟩

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