  The global expansion of maritime activities and the development of the
Automatic Identification System (AIS) have driven the advances in maritime
monitoring systems in the last decade. Monitoring vessel behavior is
fundamental to safeguard maritime operations, protecting other vessels sailing
the ocean and the marine fauna and flora. Given the enormous volume of vessel
data continually being generated, real-time analysis of vessel behaviors is
only possible because of decision support systems provided with event and
anomaly detection methods. However, current works on vessel event detection are
ad-hoc methods able to handle only a single or a few predefined types of vessel
behavior. Most of the existing approaches do not learn from the data and
require the definition of queries and rules for describing each behavior. In
this paper, we discuss challenges and opportunities in classical machine
learning and deep learning for vessel event and anomaly detection. We hope to
motivate the research of novel methods and tools, since addressing these
challenges is an essential step towards actual intelligent maritime monitoring
systems.
