An on-line algorithm for anomaly detection in trajectory data
Oren Rosen, Alexander Vladimirovich Medvedev · 2012
An algorithm for anomaly detection in trajectory data is presented. The algorithm has an intrinsic capability of handling spatial and temporal data shifts as well as dealing with trajectories of unequal lengths and, possibly, non-uniformly sampled in time. Further, it has low computational complexity and can be used in an on-line setting. The main idea of the algorithm is to extract a mean path that is “normal” for the monitored route, and with respect to the mean path, calculate the anomaly score of an acquired trajectory by means of a statistical test. The algorithm is evaluated for a simulated test scenario, where it finds all anomalous trajectories while raising no false alarms. A test on a real data set, containing trajectories of freight ships traveling through the English Channel, also proves the algorithm to perform well.