Context learning from a ship trajectory cluster for anomaly detection

This paper presents a context information extraction process over Automatic Identification System (AIS) real-world ship data, building a system with the capability to extract representative points of a trajectory cluster. With the trajectory cluster, the study proposes the use of trajectory segmentation algorithms to extract repre-sentative points of each trajectory and then use the k-means algorithm to obtain a series of centroids over all the representative points. These centroids, combined, form a new representative trajectory of the cluster. This new representative trajectory of the input cluster represents new contextual information extracted from the original set of trajectories, being possible to apply anomaly detection approaches over the new obtained context. The results show a suitable approach with several compression algorithms that are compared with a metric based on the Perpendicular Euclidean Distance.

Automated summary

This paper presents a context information extraction process based on actual ship data from AIS, extracting representative points of each trajectory using trajectory segmentation algorithms and obtaining a series of centroids using the k-means algorithm. These centroids form a new representative trajectory that can extract new contextual information from the original set of trajectories, allowing the application of anomaly detection approaches.