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

--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, then uses a contrario detection to detect abnormal events. The neural network helps us capture complex and heterogeneous patterns in vessels' behaviors, while the a contrario detection takes into account the fact that the learned 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. Nowadays, about 90% of the world trade is carried by maritime traffic, and it is growing consistently [2]. Maritime surveillance and Maritime Situational A wareness (MSA) are vital demands. In this context, anomaly detection is one of the most important tasks, because anomalies may involve accidents (loss of navigation, damages in engine, etc.) or illegal activities (smuggling, illegal transshipment, etc.). Initially designed for collision avoidance, the Automatic Identification System (AIS) has quickly become the main source of information for maritime surveillance thanks to its information richness. This paper is an extension of the MultitaskAIS presented in [1]. While [1] presents the ability of handling noisy and irregularly sampled data as well as the computational benefit of this architecture for multiple tasks in maritime surveillance, this paper focuses on detailing the most important task: anomaly detection.