Maritime anomaly detection in ferry tracks

Abstract

This paper proposes a methodology for the automatic detection of anomalous shipping tracks traced by ferries. The approach comprises a set of models as a basis for outlier detection: 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 Deviation to account for contaminated training data. The methodology 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.