Improving maritime anomaly detection and situation awareness through interactive visualization

Surveillance of large land, air or sea areas with a multitude of sensor and sensor types typically generates huge amounts of data. Human operators trying to establish individual or collective maritime situation awareness are often overloaded by this information. In order to help them cope with this information overload, we have developed a combined methodology of data visualization, interaction and mining techniques that allows filtering out anomalous vessels, by building a model over normal behavior from which the user can detect deviations. The methodology includes a set of interactive visual representations that support the insertion of the user’s knowledge and experience in the creation, validation and continuous update of the normal model. Additionally, this paper presents a software prototype that implements the suggested methodology.
Best Student Paper Award.