| Title: |
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection |
| Authors: |
Nguyen, Duong; Vadaine, Rodolphe; Hajduch, Guillaume; Garello, René; Fablet, Ronan |
| Contributors: |
Département Mathematical and Electrical Engineering (IMT Atlantique - MEE); IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); GEVES Station nationale d'essais de semences; Institut National de la Recherche Agronomique (INRA); Collecte Localisation Satellites (CLS); Département lmage et Traitement Information (IMT Atlantique - ITI); Equipe Observations Signal & Environnement (Lab-STICC_OSE); Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC); École Nationale d'Ingénieurs de Brest (ENIB); Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Bretagne Sud (UBS)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB); Institut Mines-Télécom Paris (IMT); ANR-10-LABX-0007,COMIN Labs,Digital Communication and Information Sciences for the Future Internet(2010) |
| Source: |
ISSN: 1524-9050 ; IEEE Transactions on Intelligent Transportation Systems ; https://hal.science/hal-02388260 ; IEEE Transactions on Intelligent Transportation Systems, 2021, ⟨10.1109/TITS.2021.3055614⟩. |
| Publisher Information: |
CCSD; IEEE |
| Publication Year: |
2021 |
| Collection: |
Université de Bretagne Occidentale: HAL |
| Subject Terms: |
a contrario detection; Index Terms-AIS; variational recurrent neural networks; anomaly detection; deep learning; maritime surveillance; AIS; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Description: |
International audience ; 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 \textit{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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1109/TITS.2021.3055614 |
| Availability: |
https://hal.science/hal-02388260; https://hal.science/hal-02388260v4/document; https://hal.science/hal-02388260v4/file/T_ITS_19_12_1491.pdf; https://doi.org/10.1109/TITS.2021.3055614 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.FE7D971E |
| Database: |
BASE |