Maritime Anomaly Detection using Density-based Clustering and Recurrent Neural Network
Maritime anomaly detection can improve the situational awareness of vessel traffic supervisors and reduce maritime accidents. In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algor...
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| Veröffentlicht in: | Journal of navigation 2019-07, Vol.72 (4), p.894-916 |
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| container_title | Journal of navigation |
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| creator | Zhao, Liangbin Shi, Guoyou |
| description | Maritime anomaly detection can improve the situational awareness of vessel traffic supervisors and reduce maritime accidents. In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a recurrent neural network is presented. In the method presented, the parameters of the DBSCAN algorithm were determined through statistical analysis, and the results of clustering were taken as the traffic patterns to train a recurrent neural network composed of Long Short-Term Memory (LSTM) units. The neural network was applied as a vessel trajectory predictor to conduct real-time maritime anomaly detection. Based on data from the Chinese Zhoushan Islands, experiments verified the applicability of the proposed method. The results show that the proposed method can detect anomalous behaviours of a vessel regarding speed, course and route quickly. |
| doi_str_mv | 10.1017/S0373463319000031 |
| format | Article |
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In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a recurrent neural network is presented. In the method presented, the parameters of the DBSCAN algorithm were determined through statistical analysis, and the results of clustering were taken as the traffic patterns to train a recurrent neural network composed of Long Short-Term Memory (LSTM) units. The neural network was applied as a vessel trajectory predictor to conduct real-time maritime anomaly detection. Based on data from the Chinese Zhoushan Islands, experiments verified the applicability of the proposed method. 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Navigation</addtitle><date>2019-07</date><risdate>2019</risdate><volume>72</volume><issue>4</issue><spage>894</spage><epage>916</epage><pages>894-916</pages><issn>0373-4633</issn><eissn>1469-7785</eissn><abstract>Maritime anomaly detection can improve the situational awareness of vessel traffic supervisors and reduce maritime accidents. In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a recurrent neural network is presented. In the method presented, the parameters of the DBSCAN algorithm were determined through statistical analysis, and the results of clustering were taken as the traffic patterns to train a recurrent neural network composed of Long Short-Term Memory (LSTM) units. The neural network was applied as a vessel trajectory predictor to conduct real-time maritime anomaly detection. 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| ispartof | Journal of navigation, 2019-07, Vol.72 (4), p.894-916 |
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| language | eng |
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| source | Cambridge University Press Journals Complete |
| subjects | Accidents Algorithms Anomalies Behavior Classification Clustering Density Detection Learning Methods Neural networks Noise Pattern recognition Principal components analysis Real time Recurrent neural networks Situational awareness Statistical analysis Statistical methods Supervisors Surveillance Vessels Water area |
| title | Maritime Anomaly Detection using Density-based Clustering and Recurrent Neural Network |
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