GeoTrackNet—A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection
- Resource Type
- Periodical
- Authors
- Nguyen, D.; Vadaine, R.; Hajduch, G.; Garello, R.; Fablet, R.
- Source
- IEEE Transactions on Intelligent Transportation Systems IEEE Trans. Intell. Transport. Syst. Intelligent Transportation Systems, IEEE Transactions on. 23(6):5655-5667 Jun, 2022
- Subject
- Transportation
Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Artificial intelligence
Anomaly detection
Trajectory
Probabilistic logic
Task analysis
Geospatial analysis
Detectors
AIS
maritime surveillance
deep learning
anomaly detection
variational recurrent neural networks
a contrario detection
- Language
- ISSN
- 1524-9050
1558-0016
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach—referred to as GeoTrackNet —for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks and a contrario detection to detect abnormal events. The neural network provides a new means to capture complex and heterogeneous patterns in vessels’ behaviours, while the a contrario detector takes into account the fact that the learnt distribution may be location-dependent. Experiments on a real AIS dataset comprising more than 4.2 million AIS messages demonstrate the relevance of the proposed method compared with state-of-the-art schemes.