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A comprehensive survey on the applications of machine learning techniques on maritime surveillance to detect abnormal maritime vessel behaviors
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Employed deep learning can lead to numerous false alarms if there is poor alignment between environment and algorithm.
“Furthermore, employed deep learning can lead to numerous false alarms if there is a poor alignment between the environment and the algorithm in [160].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [160]
Citations
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References 58 · Cited by 44
Research works printed but not linked: 16
- Castaldo F, Palmieri FA, Bastani V, et al (2014) Abnormal vessel behavior detection in port areas based on dynamic Bayesian networks. In: 17th International Conference on Information Fusion (FUSION), IEEE, pp 1–7
- Coraluppi S, Carthel C, Braca P, et al (2016) The mixed Ornstein-Uhlenbeck process and context exploitation in multi-target tracking. In: 2016 19th International Conference on Information Fusion (FUSION), IEEE, pp 217–224
- Du Toit J, Van Vuuren J (2014) Semi-automated maritime vessel activity detection using hidden Markov models. In: Proceedings of the 43rd annual conference of the operations research society of South Africa, Parys, pp 71–78
- Fraccaro M, Sønderby SK, Paquet U, et al (2016) Sequential neural models with stochastic layers. arXiv preprint arXiv:1605.07571 · arXiv
- Kowalska K, Peel L (2012) Maritime anomaly detection using Gaussian process active learning. In: 2012 15th International Conference on Information Fusion, IEEE, pp 1164–1171
- Laxhammar R (2008) Anomaly detection for sea surveillance. In: 2008 11th international conference on information fusion, IEEE, pp 1–8
- Maddison CJ, Lawson D, Tucker G, et al (2017) Filtering variational objectives. arXiv preprint arXiv:1705.09279 · arXiv
- Malhotra P, Vig L, Shroff G, et al (2015) Long short term memory networks for anomaly detection in time series. In: Proceedings, Presses universitaires de Louvain, pp 89–94
- Mirghasemi S, Parand I (2013) A parallel approach to combine SVM, edge and corner detection methods for target detection. J Multim Process Technol 4(2):47–54
- Pallotta G, Vespe M, Bryan K (2013) Traffic route extraction and anomaly detection from AIS data. In: International COST MOVE Workshop on Moving Objects at Sea, Brest, France
- Singh A, Thakur N, Sharma A (2016) A review of supervised machine learning algorithms. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), Ieee, pp 1310–1315
- Singh SK, Heymann F (2020) Machine learning-assisted anomaly detection in maritime navigation using AIS data. 2020 IEEE/ION Position. Location and Navigation Symposium (PLANS), IEEE, pp 832–838
- Varlamis I, Tserpes K, Etemad M, et al (2019) A network abstraction of multi-vessel trajectory data for detecting anomalies. In: EDBT/ICDT Workshops
- Wang G, Malik A, Yau C, etal (2017) TraSeer: a visual analytics tool for vessel movements in the coastal areas. In: 2017 IEEE International Symposium on Technologies for Homeland Security (HST), IEEE, pp 1–6
- Woodill AJ, Kavanaugh M, Harte M, et al (2020) Predicting illegal fishing on the Patagonia Shelf from oceanographic seascapes. arXiv preprint arXiv:2007.05470 · arXiv
- Zhang Y, Zheng Z, Xiang C (2020) Analysis of vessel abnormal behavior detection. In: 2020 2nd International Conference on Robotics Systems and Vehicle Technology, pp 45–53
Metadata
- Authors
- Gamage, C., Dinalankara, R., Samarabandu, J., & Subasinghe, A.
- Affiliations
- University of Sri Jayewardenepura (LK)
- Western University (CA)
- Venue
- WMU Journal of Maritime Affairs
- Published
- 2023-06-27
- Volume
- 22
- Issue
- 4
- Pages
- 447-477
- ISSN
- 1651-436X (ISSN-L)
- 1654-1642
- OpenAlex
- W4382248960
- Cited by
- 44
- References
- 58
- Citations by year
- 2026: 7 · 2025: 18 · 2024: 16 · 2023: 3
- FWCI
- 3.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- WMU Journal of Maritime Affairs (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Social Sciences › Political Science and International Relations › Maritime Security and History
- Keywords
- maritime surveillance, maritime security, supervised learning, unsupervised learning, deep learning, convolutional neural networks, long short-term memory, support vector machine
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