Maritime situation analysis framework: Vessel interaction classification and anomaly detection

Maritime domain awareness is critical for protecting sea lanes, ports, harbors, offshore structures like oil and gas rigs and other types of critical infrastructure against common threats and illegal activities. Typical examples range from smuggling of drugs and weapons, human trafficking and piracy all the way to terror attacks. Limited surveillance resources constrain maritime domain awareness and compromise full security coverage at all times. This situation calls for innovative intelligent systems for interactive situation analysis to assist marine authorities and security personal in their routine surveillance operations. In this article, we propose a novel situation analysis approach to analyze marine traffic data and differentiate various scenarios of vessel engagement for the purpose of detecting anomalies of interest for marine vessels that operate over some period of time in relative proximity to each other. We consider such scenarios as probabilistic processes and analyze complex vessel trajectories using machine learning to model common patterns. Specifically, we represent patterns as left-to-right Hidden Markov Models and classify them using Support Vector Machines. To differentiate suspicious activities from unobjectionable behavior, we explore fusion of data and information, including kinematic features, geospatial features, contextual information and maritime domain knowledge. Our experimental evaluation shows the effectiveness of the proposed approach using comprehensive real-world vessel tracking data from coastal waters of North America.