Abstract
This paper proposes a methodology for the automatic detec- tion of anomalous shipping tracks traced by ferries. The ap- proach comprises a set of models as a basis for outlier detec- tion: A Gaussian process (GP) model regresses displacement information collected over time, and a Markov chain based detector makes use of the direction (heading) information. GP regression is performed together with Median Absolute Devi- ation to account for contaminated training data. The method- ology utilizes the coordinates of a given ferry recorded on a second by second basis via Automatic Identification System. Its effectiveness is demonstrated on a dataset collected in the Solent area.