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Synthetic trajectory extraction for maritime anomaly detection

: Anneken, M.; Jousselme, A.-L.; Robert, S.; Beyerer, J.


Institute of Electrical and Electronics Engineers -IEEE-:
International Conference on Computational Science and Computational Intelligence, CSCI 2018. Proceedings : Las Vegas, NV, USA, 13-15 December 2018
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2018
ISBN: 978-1-7281-1360-9
ISBN: 978-1-7281-1361-6
International Conference on Computational Science and Computational Intelligence (CSCI) <5, 2018, Las Vegas/Nev.>
Conference Paper
Fraunhofer IOSB ()

In the maritime domain, the main goal for large vessels is to drive as fast and efficiently as possible from one harbour to the next. This results in vessels following quite distinct routes. Deviations from these routes can be seen as indicators for illegal or at least suspicious behaviour. In order to extract these routes, clustering algorithms can be used, that will store the traffic patterns as synthetic trajectories. A vessel can be assigned to a specific route or a deviation from the routes can be detected by evaluating these route models. A challenge in designing the synthetic trajectories lies in the faithful representation, sparsity of data, treatment of outliers and split-up of routes. Here, a novel approach to model the uncertainty of the vessels movements while following a route is introduced: A cluster is now represented by a trajectory consisting of segments. Due to obstacles like shallow waters, a route might split up which will generate sub-segments. Instead of storing all cluster points, the position, speed, and course of the vessels are attached to each segment in form of normal distributions. Using these attributes, the probability of a deviation from the given trajectory can be estimated.