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. 

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Keyword

anomaly detection

interaction

visualization

situation awareness

visual data mining

visual analytics

Technology

Teknik

Publication and Content Type

ref (subject category)

kon (subject category)