A framework for anomaly detection in maritime traj

Authors: PoRuey Lei

Publish Date: 2015/05/19

Volume: 47, Issue: 1, Pages: 189-214

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Abstract

Rapid growth in location data acquisition techniques has led to a proliferation of trajectory data related to moving objects This large body of data has expanded the scope for trajectory research and made it applicable to a more diverse range of fields However data uncertainty which is naturally inherent in the trajectory data brings the challenge in trajectory data mining and affects the quality of the results Specifically unlike trajectory collected from vehicles moving along road networks trajectory data generated by vessels moving free in maritime space have increased the difficulty of sea traffic analysis and anomalous behavior detection Furthermore due to the huge volume and complexity of maritime trajectory data it is hard to define the abnormality of movement behavior and detect anomalies Additionally traditional analysis and evaluation by human intelligence is overloaded with the dramatic increasing in amount of maritime trajectory data and is an inefficient approach Thus an effective automated method for mining trajectory data and detecting anomalies would be a valuable contribution to maritime surveillance This paper explores the maritime trajectory data for anomalous behavior detection We propose a framework for maritime trajectory modeling and anomaly detection called MTMAD Our model takes into account the fact that anomalous behavior manifests in unusual location points and subtrajectories in the spatial domain as well as in the sequence and manner in which these locations and subtrajectories occur This study therefore began by identifying outlying features required for anomaly detection including spatial sequential and behavioral features We then explore the movement behavior from historical trajectories and build a maritime trajectory model for anomaly detection The proposed model accurately describes movement behavior and captures outlying features in trajectory data We then developed an anomaly detection algorithm based on this model in which an indicator is used to evaluate suspicious behavior and scores trajectory behavior according to the defined outlying features Experiment results demonstrate that the proposed MTMAD framework is capable of effectively detecting anomalies in maritime trajectories

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