False alarm reduction in maritime anomaly detection with contextual verification

Automated vessel anomaly detection is immensely important for preventing and reducing illegal activities (e.g., drug dealing) and for effective emergency response and rescue in a country’s territorial waters. A major limitation of previously proposed vessel anomaly detection techniques is the high rate of false alarms as these methods mainly consider vessel kinematic information which is generally obtained from AIS data. In many cases, an anomalous vessel in terms of kinematic data can be completely normal and legitimate if the “context” at the location and time (e.g., weather and sea conditions) of the vessel is factored in. We propose a novel anomalous vessel detection framework that utilizes such contextual information to reduce false alarms through “contextual verification”. We evaluate our proposed framework for vessel anomaly detection using real-life AIS data sets obtained from U.S. Coast Guard.