issn 1650-8580
isbn 978-91-7668-810-6 Örebro Studies in Technology 50örebro 2011
Doctoral Dissertation
Anomaly Detection in the Surveillance Domain
Christoffer Brax Computer Science 2011
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ÖREBRO STUDIES IN TEchNOlOgy 50Christoffer Brax is part of the information fusion research program at the University of Skövde, Sweden. He is also a member of the Skövde Artificial Intelligence Lab, SAIL. His main research interests are high-level information fusion and data mining, and the applica-tion of anomaly detecapplica-tion.
In this doctoral thesis, Brax looks at the problem of how to detect anomalies in the surveillance domain. This is done by a characterisation of the surveillance domain and a literature review that identifies a number of weaknesses in previous anomaly detection methods used in the surveillance domain. Based on the findings from this study, a new anomaly detection method is proposed. The proposed method is evaluated with respect to detection performance and computational cost on a number datasets, recorded from real-world sensors, in different application areas of the surveillance domain. Additionally, the method is compared to two other commonly used anomaly detection methods. Finally, the method is evaluated on a dataset with anomalies developed together with maritime subject mat-ter experts. The conclusion of the thesis is that the proposed method has a number of strengths compared to previous methods and is suitable for use in operative maritime command and control systems.
