AIS-based kinematic anomaly classification for maritime surveillance

Anomaly detection is crucial for maritime surveillance and law enforcement. Early identification of abnormal behavior ensures maritime order and fosters a safer environment for maritime traffic. However, anomaly identification and classification methods often suffer from vagueness because of the complexity of anomalies, limiting their effectiveness. We propose a systematic and data-driven framework for kinematic anomaly classification and detection. Through extensive inspection and experiments, a comprehensive characterization of diverse anomalies is provided to classify vessel kinematic anomalies into three categories, including Speed and Course Anomaly (SCA), Turning Anomaly (TA) and Loitering Anomaly (LA), along with detection methods that are tailored to each anomaly type to facilitate the production of high-quality anomaly labels. Subsequently, supervised training is exploited for anomaly classification. The effectiveness of the proposed method is validated using the open Automatic Identification System (AIS) dataset provided by the Danish Maritime Authority (DMA). With proper feature design, the proposed method can achieve a classification accuracy of approximately 99% using simple neural networks.