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Machine Learning Approaches to Maritime Anomaly Detection (CROSBI ID 212708)

Prilog u časopisu | pregledni rad (znanstveni) | međunarodna recenzija

Obradović, Ines ; Miličević, Mario ; Žubrinić, Krunoslav Machine Learning Approaches to Maritime Anomaly Detection // Naše more : znanstveni časopis za more i pomorstvo, 61 (2014), 5-6; 96-101

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

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Podaci o izdanju

61 (5-6)

2014.

96-101

objavljeno

0469-6255

Povezanost rada

Računarstvo, Tehnologija prometa i transport

Poveznice
Indeksiranost