Machine Learning Approaches to Maritime Anomaly Detection (CROSBI ID 212708)
Prilog u časopisu | pregledni rad (znanstveni) | međunarodna recenzija
Podaci o odgovornosti
Obradović, Ines ; Miličević, Mario ; Žubrinić, Krunoslav
engleski
Machine Learning Approaches to Maritime Anomaly Detection
Topics related to safety in maritime transport have become very important over the past decades due to numerous maritime problems putting both human lives and the environment in danger. Recent advances in surveillance technology and the need for better sea traffic protection led to development of automated solutions for detecting anomalies. These solutions are based on generating normality models from data gathered on vessel movement, mostly from AIS. This paper provides a presentation of various machine learning approaches for anomaly detection in the maritime domain. It also addresses potential problems and challenges that could get in the way of successful automation of such systems.
maritime traffic ; anomaly detection ; situational awareness ; machine learning ; AIS
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
Povezanost rada
Računarstvo, Tehnologija prometa i transport