  The worldwide growth of maritime traffic and the development of the Automatic
Identification System (AIS) has led to advances in monitoring systems for
preventing vessel accidents and detecting illegal activities. In this work, we
describe research gaps and challenges in machine learning for vessel behavior
change and event detection, considering several constraints imposed by
real-time data streams and the maritime monitoring domain. As a starting point,
we investigate how unsupervised and semi-supervised change detection methods
may be employed for identifying shifts in vessel behavior, aiming to detect and
label unusual events.
