A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics

With the recent increase in the use of sea transportation, the importance of maritime surveillance for detecting unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by 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 confuse operators, inducing errors when evaluating ship activities and tagging them as unusual. Using domain knowledge to classify activities as anomalous is essential in the maritime navigation environment since there is a well-known lack of labeled data in this domain. In an area where identifying anomalous trips is a challenging task using solely automatic approaches, we use visual analytics to bridge this gap by utilizing users' reasoning and perception abilities. In this work, we propose a visual analytics tool that uses spatial segmentation to divide trips into subtrajectories and score them. These scores are displayed in a tabular visualization where users can rank trips by segment to find local anomalies. The amount of interpolation in subtrajectories is displayed together with scores so that users can use both their insight and the trip displayed on the map to determine if the score is reliable.