Abnormal vessel behavior detection in port areas based on Dynamic Bayesian Networks

Automatic recognition of abnormal situations in harbor environments is approached in this paper with a system based on Dynamic Bayesian Networks. The area under surveillance is partitioned in zones of different sizes and shapes by means of an Instantaneous Topological Map, on which events are detected and inference is carried out. The model is trained with synthetic normal trajectories of ships and vessels mooring in the port, and each time a new trajectory is presented to the system, comparisons with the normal behaviors stored in the network are performed. If no match is found, an abnormal situation is declared and countermeasures can be taken. The algorithm has been tested in a real port with simulated data in order to evaluate the false alarm rate and the abnormal detection capabilities of the proposed approach.