Rotation- and Scale-Invariant Shape Extraction from Vessel Trajectories for Human-in-The-Loop Monitoring: A Case Study at the Ports of Brittany

Maritime vessel monitoring is vital for ensuring navigational safety, protecting marine ecosystems, and enforcing regulations. This work presents a framework to support expert analysis and monitoring of vessel activities using AIS trajectory data. By extracting rotation- and scale-invariant shape signatures through a relative Hough transform, our system clusters and organizes subtrajectory patterns, enabling intuitive visual exploration. Experts interactively associate representative shapes with maritime events such as trawling or port visits, creating an event-to-shape map used for real-time detection in new trajectories. The framework efficiently captures geometric and motion dynamics, supports interpretable, trustworthy AI through human-in-the-loop validation, and facilitates labeled data creation to enhance future automated analysis. We demonstrate its effectiveness with a case study using real-world data of fishing vessels collected from the ports of Britannia, highlighting its potential for scalable, human-in-the-loop maritime surveillance.