Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ACC.2012.6315346 | American Control Conference |
Keywords | Field | DocType |
computational complexity,data handling,freight containers,ships,statistical testing,English channel,anomalous trajectories,anomaly detection,anomaly score,computational complexity,freight ship trajectory,mean path extraction,online algorithm,spatial data shifts,statistical test,temporal data shifts,trajectory data | Anomaly detection,Computer science,Algorithm,Communication channel,Temporal database,Scenario testing,Group method of data handling,Statistical hypothesis testing,Trajectory,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
0743-1619 E-ISBN : 978-1-4673-2102-0 | 978-1-4673-2102-0 | 5 |
PageRank | References | Authors |
0.47 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olov Rosen | 1 | 23 | 4.10 |
| Alexander Medvedev | 2 | 72 | 22.43 |