LOCAL ANOMALY DETECTION IN MARITIME TRAFFIC USING VISUAL ANALYTICS

Abstract

With the recent increase in sea transportation usage, the importance of maritime surveillance to detect unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by the surveillance systems are often incomplete, creating a need for the data gaps to be filled using techniques such as interpolation methods. However, such approaches do not decrease the uncertainty of ship activities. Depending on the frequency of the data generated, they may even make the operators more confused, inducing them to errors when evaluating ship activities to tag them as unusual. Using domain knowledge to classify activities as anomalous is essential in the maritime navigation environment since there is a wellknown lack of labeled data in this domain. In an area where finding which trips are anomalous is a challenging task using solely automatic approaches, we use visual analytics to bridge this gap by utilizing users’ reasoning and perception abilities. In the current work, we investigate existing work that focuses on finding anomalies in vessel trips and how they improve the user understanding of the interpolated data. We then propose and develop a visual analytics tool that uses spatial segmentation to divide trips into subtrajectories and give a score for each subtrajectory. We then display these scores in tabular visualization where users can rank by segment to find local anomalies. We also display the amount of interpolation in subtrajectories with the score so users can use their insight and the trip display on the map to make sense if the score is reliable. We did a user study to assess our tool’s usability and the preliminary results showed that users were able to identify anomalous trips.