Maritime anomaly detection is a key technique in intelligent vessel traffic surveillance systems and implementation of maritime situational awareness. In this paper, we propose a method which combines vessel trajectory clustering and Naïve Bayes classifier to detect anomalous vessel behaviour in the maritime surveillance system. A similarity measurement between vessel trajectories is designed based on the spatial and directional characteristics of Automatic Identification System (AIS) data, then the method of hierarchical and k-medoids clustering are applied to model and learn the typical vessel sailing pattern within harbour waters. The Naïve Bayes classifier of vessel behaviour is built to classify and detect anomalous vessel behaviour. The proposed method has been tested and validated on the vessel trajectories from AIS data within the waters of Xiamen Bay and Chengsanjiao, China. The results indicate that the proposed method is effective and helpful, thus enhancing maritime situational awareness in coastal waters.

The trajectory data of moving objects contain huge amounts of information pertaining to traffic flow. It is incredibly important to extract valuable knowledge from this particular kind of data. Trajectory clustering is one of the most widely used approaches to complete this extraction. However, the current practice of trajectory clustering always groups similar subtrajectories that are partitioned from the trajectories; these methods would thus lose important information of the trajectory as a whole. To deal with this problem, this paper introduces a new trajectory-clustering algorithm based on sampling and density, which groups similar traffic movement tracks (car, ship, airplane, etc.) for further analysis of the characteristics of traffic flow. In particular, this paper proposes a novel technique of measuring distances between trajectories using point sampling. This distance measure does not divide the trajectory and thus conserves the integrated knowledge of these trajectories. This trajectory clustering approach is a new adaptation of a density-based clustering algorithm to the trajectories of moving objects. This paper then adopts the entropy theory as the heuristic for selecting the parameter values of this algorithm and the sum of the squared error method for measuring the clustering quality. Experiments on real ship trajectory data have shown that this algorithm is superior to the classical method TRACLUSS in the run time and that this method works well in discovering traffic flow patterns.

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The regional ship collision risk assessment for multiple ships in restricted waters is of great significance to the early warning of ship collision risk and the intelligent supervision of maritime traffic. Given the existed method of regional ship collision risk assessment without considering the impact of ship aggregation density, this paper proposes a novel regional ship collision risk assessment method that considers the aggregation density (AD) of the clusters of encounter ships (CES) for intelligent surveillance and navigation. The effectiveness of the proposed method has been examined by the experimental case study in the waters of Xiamen, China, and analysis has been compared with other existed studies to show the advantages of the new proposed algorithm. The results show that the study method can more intuitively and effectively quantify the temporal and spatial distribution of regional collision risks in the restricted sea area. The proposed method can improve the efficiency of traffic management when monitoring the ship collision risks in macroscopic view, and assist the safety of manned and unmanned ship navigation.