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Identifying kinematic anomalies is critical for maritime safety, especially in high-traffic areas. This paper presents a novel, lightweight, scalable method for trajectory clustering and kinematic anomaly detection using inflection-point sequences across varying trajectory lengths and environments. By encoding trajectory shape changes into compact fingerprints, the technique circumvents the computational burdens of deep learning. Results indicate that the proposed fingerprinting method can differentiate anomalous driving patterns, offering a viable solution for real-time anomaly detection. This study presents a simple yet robust trajectory analysis framework applicable to personal watercraft monitoring and to larger maritime vessels, paving the way for improved safety and real-time decision-support systems. Trajectories are segmented, inflection points are extracted, and clustering is performed using trajectory fingerprints. Human evaluation experiments validate the approach aligned with Intelligent Distance Control (IDC), and a larger window size improves accuracy to 85% or higher when aligned with the rate of turn (ROT).
personal watercraft; trajectory; classification; clustering
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