Abstract
This paper presents a novel machine learning-based approach for detecting abnormal ship movements using CCTV videos. Our method utilizes graph-based algorithms to analyze ship trajectories and identify anomalies, with a focus on enhancing maritime safety and accident prevention. Unlike conventional AIS data-dependent methods, our approach directly detects and visualizes abnormal movements from CCTV videos, particularly in narrow coastal areas. We evaluate the proposed method using real-world CCTV video data and demonstrate its effectiveness in detecting abnormal ship movements, offering promising results in real-world scenarios. The findings of this study have important implications to improve maritime safety and prevent accidents.
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