Ship anomalous behavior detection based on interval prediction of multiple vessel trajectories
Published in Engineering Applications of Artificial Intelligence, 2025
In response to the increasing complexity of maritime traffic situations, effective maritime surveillance is essential to ensure the safety of maritime activities. This paper leverages Automatic Identification Systems (AIS) data in conjunction with deep learning techniques and statistical methods to achieve adaptive matching and prediction of multiple vessel trajectories, as well as the identification of anomalous vessel behaviors. Initially, an improved Hausdorff distance is employed to accommodate preprocessed trajectory data, and an enhanced Density-Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm is utilized for cluster analysis, facilitating adaptive multi-vessel trajectory matching. Subsequently, considering the inter-vessel influence zones, a multi-vessel behavior feature prediction is realized based on the completed adaptive vessel trajectory matching. Furthermore, distribution characteristics of …
Recommended citation: W Liu, C Chen, Y Peng, M Liang. (2025). Ship anomalous behavior detection based on interval prediction of multiple vessel trajectories. Engineering Applications of Artificial Intelligence, 160, 111693. https://www.sciencedirect.com/science/article/pii/S0952197625016951