This paper compares a number of state-of-the-art anomaly detection methods on real ship trajectories obtained by an Automatic Identification System (AIS) in the Baltic sea. Because most methods need fixed length trajectory representations, the paper also gives some solutions for reducing variable length trajectories to a fixed size.
AnnekenM.FischerY. and BeyererJ., Evaluation and comparison of anomaly detection algorithms in annotated datasets from the maritime domain, SAI Intelligent Systems Conference. Proceedings, London, 2015, pp. 169–178.
2.
BaldiP., Autoencoders, Unsupervised Learning, and Deep Architectures, Workshop on Unsupervised and Transfer Learning, 2012, pp. 37–50.
3.
ChandolaV.BanerjeeA. and KumarV., Anomaly Detection: A Survey, ACM Computing Surveys, 2009, pp. 1–72.
4.
ChenL.ÖzsuM.T. and OriaV., Robust and fast similarity search for moving object trajectories, In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, 2005, pp. 491–502.
5.
EsterM.KriegelH.SanderJ. and XuX., A density-based algorithm for discovering clusters in large spatial databases with noise, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), AAAI Press, 1996, pp. 226–231.
6.
FischerA. and IgelC., Training Restricted Boltzmann Machines: An Introduction, Pattern Recognition47 (2014), 25–39.
7.
GersF.A.SchmidhuberJ. and CumminsF., Learning to Forget: Continual Prediction with LSTM, Neural Computation12(10) (2014), 2451–2471.
8.
HintonG., A Practical Guide to Training Restricted Boltzmann Machines, UTML TR 2010–03, University of Toronto, 2010.
9.
LaneR.O.NevellD.A.HaywardS.D. and BeaneyT.W., Maritime anomaly detection and threat assessment, 13th Conference on Information Fusion (FUSION 2010), 2010, pp. 1–8.
10.
LaxhammarR.FalkmanG. and SviestinsE., Anomaly detection in sea traffic – A comparison of the Gaussian Mixture Model and the Kernel Density Estimator, Information fusion – Fusion’09, 12th International conference on Information Fusion, 2009, pp. 756–763.
11.
LaxhammarR., Anomaly Detection in Trajectory Data for Surveillance Applications, Licentiate thesis, Orebro University, 2011.
12.
LaxhammarR. and FalkmanG., Sequential Conformal Anomaly Detection in Trajectories based on Hausdorff Distance, 14th International Conference on Information Fusion, Chicago, Illinois, USA, 2011, pp. 153–160.
13.
LiX.HanJ.KimS. and GonzalezH., Roam: Rule- and motif-based anomaly detection in massive moving object data sets, In Proceedings of 7th SIAM International Conference on Data Mining, 2007, pp. 273–84.
14.
LiuB.de SouzaE.N.MatwinS. and SydowM., Knowledge-based Clustering of Ship Trajectories Using Density-based Approach, IEEE International Conference on Big Data, 2014, pp. 603–608.
15.
LiuB., Maritime Traffic Anomaly Detection From AIS Satellite Data in Near Port Regions, Master’s thesis, Dalhousie University, Halifax, Nova Scotia, 2015.
16.
LiuB.de SouzaE.N.HilliardC. and MatwinM., Ship Movement Anomaly Detection Using Specialized Distance Measures, 18th International Conference on Information Fusion, Washington, 2015.
17.
MalhotraP.VigL.ShroffG.AgarwalP., Long Short Term Memory Networks for Anomaly Detection in Time Series, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2015, pp. 89–94.
18.
MartineauE. and RoyJ., Maritime anomaly detection: Domain introduction and review of selected literature, Defence R&D Canada – Valcartier, Technical Memorandum, DRDC Valcartier TM 2010-460, 2011.
19.
MascaroS.NicholsonA.E. and KorbK.B., Anomaly detection in vessel tracks using Bayesian networks, International Journal of Approximate Reasoning55(1) (2014), 84–98.
20.
MorrisB. and TrivediM., survey of vision-based trajectory learning and analysis for surveillance, IEEE Trans Circuits Syst for Video Technol18 (2008), 1114–127.
21.
NaftelA. and KhalidS., Classifying spatiotemporal object trajectories using unsupervised classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space, Multimedia Systems12(3) (2008), 227–38.
22.
OlivaJ.B., Anomaly Detection and Modeling of Trajectories, Master’s thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, 2012.
23.
O’SheaT.JClancyT.C. and McGwierR.W., Recurrent Neural Radio Anomaly Detection, arXiv:1611.00301, 2016.
24.
PallottaG.VespeM. and BryanK., Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction, Entropy15(6) (2013), 2218–2245.
25.
PiciarelliC. and ForestiG.L., Anomalous trajectory detection using support vector machines, In Advanced Video and Signal Based Surveillance, IEEE Conference, 2007, pp. 153–158.
26.
PiciarelliC.MicheloniC. and ForestiG.L., Trajectory-Based Anomalous Event Detection, IEEE Transactions on Circuits and Systems for Video Technology18(11) (2008), 1544–1554.
27.
PraczykT., Using Augmenting Modular Neural Networks to Evolve Neuro-Controllers for a Team of Underwater Vehicles, Soft Computing, DOI:10.1007%2Fs00500-014-1221-0, 2014.
28.
ReddyK.K.SarkarS.VenugopalanV. and GieringM., Anomaly Detection and Fault Disambiguation in Large Flight Data: A Multi-modal Deep Auto-encoder Approach, Annual Conference of the Prognostics and Health Management Society, 2016, pp. 1–8.
29.
SinghA., Anomaly Detection for Temporal Data using Long Short-Term Memory, Master’s Thesis at KTH Information and Communication Technology, 2017.
30.
SoleimaniB.H.de SouzaE.N.HilliardC. and MatwinS., Anomaly Detection in Maritime Data Based on Geometrical Analysis of Trajectories, 18th International Conference on Information Fusion, Washington, 2015, pp. 1100–1105.
31.
SonodaS. and MurataN., Decoding Stacked Denoising Autoencoders, eprint arXiv:1605.02832, 2016.
32.
SundholmJ., Feature Extraction for Anomaly Detection in Maritime Trajectories, Master’s thesis, KTH Computer Science and Communication, 2014.
33.
TagawaT.TadokoroY. and YairiT., Structured Denoising Autoencoder for Fault Detection and Analysis, JMLR: Workshop and Conference Proceedings, no. 39 (2014), pp. 96–111.
34.
TorresE., Applying Deep Learning Algorithms to Alarm/Anomaly Detection for Grid Jobs, SIST/GEM Final Presentations, (2017).
35.
VandecasteeleA. and NapoliA., An Enhanced Spatial Reasoning Ontology for Maritime Anomaly Detection, 7th International Conference on System Of Systems Engineering-IEEE SOSE, 2012, pp. 246–252.
36.
VincentP.LarochelleH.BengioY. and ManzagolP., Extracting and Composing Robust Features with Denoising Autoencoders, Proceedings of the 25th international conference on Machine learning, 2008, pp. 1096–1103.
37.
VovkV.GammermanA. and ShaferG., Algorithmic Learning in a Random World, Springer-Verlag New York, Inc., 2005.
38.
WangX.MaK.T.NgG.-W. and GrimsonW.E.L., Trajectory analysis and semantic region modeling using a nonparametric bayesian model, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, 2008.
39.
WuC. and GuoY., Adaptive Anomalies Detection with Deep Network, The Seventh International Conference on Advanced Cognitive Technologies and Applications, 2015, pp. 181–186.
40.
XiaY.CaoX.WenF.HuaG. and SunJ., Learning Discriminative Reconstructions for Unsupervised Outlier Removal, 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1511–1519.