Anomaly detection in vessel track data

Thesis

This thesis introduces novelty detection techniques that use a combination of Gaussian processes, extreme value theory and divergence measurement to identify anomalous behaviour in both streaming and batch marine data. The work is set in context by a review of current methodologies, identifying the limitations of current modelling processes within this domain.

Marine data modelling is first improved by endowing the Gaussian process with the capacity to model both first order and sec...

Expand abstract

Authors: Smith, M

• Publication date: 2014
• Type of award: MSc by Research
• Level of award: Masters
• Awarding institution: Oxford University, UK
• Language: English
• Keywords: Maritime Traffic; Gaussian Process; Outlier Detection; Novelty Detection; Extreme Value
• Subjects: Mathematical modeling (engineering); Information engineering; Pattern recognition (statistics); Stochastic processes; Probability