Abnormal Vessel Behavior Detection Based on AIS Data

作者: 张树波 :广州航海学院计算机系,广东 广州; 唐强荣 :广州航海学院海运系,广东 广州;

关键词: AIS数据异常行为船舶航迹行为建模异常检测AIS Data Abnormal Behavior Vessel Trajectory Behavioral Modeling Anomaly Detection


Abstract: Maritime behavior detection is critical for maritime surveillance and management. It is important for ship’s safe sailing, normal production at ports, marine environmental protection, water illegal activities prevention and so on. With more and more AIS systems are installed on board, massive amounts of AIS data have been accumulated, which provides us with promising ways to investigate the law of ship motions and the detection of abnormal behaviours. In this paper, various algorithms used for detecting abnormal behaviours of ship are reviewed and commented, and the challenge of researchers in this field faced is then pointed out, in the end, the perspectives in this realm are also proposed.

文章引用: 张树波 , 唐强荣 (2015) 基于AIS数据的船舶异常行为检测方法。 人工智能与机器人研究, 4, 23-31. doi: 10.12677/AIRR.2015.44004


[1] Le Guillarme, N. and Lerouvreur, X. (2013) Unsupervised Extraction of Knowledge from S-AIS Data for Maritime Situational Awareness. The 16th International Conference on Information Fusion (FUSION), Istanbul, July 9-12, 2025-2032.

[2] Smith, M.R.S., Roberts, S.J., et al. (2012) Online Maritime Abnormality Detection Using Gaussian Processes and Extreme Value Theory. The IEEE International Conference on Data Mining (ICDM), Brussels, 10-13 December 2012, 645-654.

[3] Edgeworth, F.Y. (1887) Xli. On Discordant Observations. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 23, 364-375.

[4] http://baike.baidu.com/

[5] Portnoy, L., Eskin, E. and Stolfo, S. (2001) Intrusion Detection with Unlabeled Data Using Clustering. Proceedings of ACM CSS Workshop on Data Mining Applied to Security, Philadelphia, November 2011, 5-8.

[6] Holst, A. and Ekman, J. (2003) Anomaly Detection in Vessel Motion. Internal Report Saab Systems, Järfälla.

[7] Laxhammar, R. (2011) Anomaly Detection in Trajectory Data for Surveillance Applications. Studies from the School of Science and Technology at Örebro University, 19.

[8] Martineau, E. and Roy, J. (2011) Maritime Anomaly Detection: Domain Introduction and Review of Selected Literature. Defense Research and Development Canada Valcartier (QUEBEC).

[9] Chandola, V., Banerjee, A. and Kumar, V. (2009) Anomaly Detection: A Survey. ACM Computing Surveys (CSUR), 41, 15.

[10] Palma, A.T., Bogorny, V., Kuijpers, B., et al. (2008) A Clustering-Based Approach for Discovering Interesting Places in Trajectories. Proceedings of the 2008 ACM Symposium on Applied Computing, 863-868.

[11] Vespe, M., Visentini, I., Bryan, K., et al. (2012) Unsupervised Learning of Maritime Traffic Patterns for Anomaly Detection. Proceedings of the 9th IET Data Fusion & Target Tracking Conference (DF & TT 2012): Algorithms & Applications, London, 16-17 May 2012, 1-5.

[12] De Vries, G.K.D. and Van Someren, M. (2012) Machine Learning for Vessel Trajectories Using Compression, Align-ments and Domain Knowledge. Expert Systems with Applications, 39, 13426-13439.

[13] Douglas, D.H. and Peucker, T.K. (1973) Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or Its Caricature. The International Journal for Geographic Information and Geovisualization, 10, 112-122.

[14] Laxhammar, R. (2008) Anomaly Detection for Sea Surveillance. Proceedings of the 11th International Conference on Information Fusion, Cologne, 30 June-3 July 2008, 1-8.

[15] 朱飞祥, 张英俊, 高宗江. 基于数据挖掘的船舶行为研究[J]. 中国航海, 2012(35): 50-54.

[16] Lee, J.G., Han, J. and Li, X. (2008) Trajectory Outlier Detection: A Partition-and-Detect Framework. Proceedings of the 24th International Conference on Data Engineering, Cancún, 7-12 April 2008, 140-149.

[17] Lee, J.G., Han, J. and Whang, K.Y. (2007) Trajectory Clustering: A Partition-and-Group Framework. Proceedings of the International Conference on Management of Data, Beijing, 12-14 June 2007, 593-604.

[18] Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T. and Maybank, S. (2006) A System for Learning Statistical Motion Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1450-1464.

[19] Li, X., Hu, W. and Hu, W. (2006) A Coarse-to-Fine Strategy for Vehicle Motion Trajectory Clustering. Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, 20-24 August 2006, 591-594.

[20] Fu, Z., Hu, W. and Tan, T. (2005) Similarity Based Vehicle Trajectory Clustering and Anomaly Detection. Proceedings of the IEEE International Conference on Image Processing, Genoa, 11-14 September 2005, II-602-5.

[21] Yang, Y., Cui, Z., Wu, J., et al. (2012) Trajectory Analysis Using Spectral Clustering and Sequence Pattern Mining. Journal of Computational Information Systems, 8, 2637-2645.

[22] Piciarelli, C. and Foresti, G.L. (2006) On-Line Trajectory Clustering for Anomalous Events Detection. Pattern Recognition Letters, 27, 1835-1842.

[23] Kraiman, J.B., Arouh, S.L. and Webb, M.L. (2002) Automated Anomaly Detection Processor. International Society for Optics and Photonics, Bellingham, 128-137.

[24] Laxhammar, R. and Falkman, G. (2011) Sequential Conformal Anomaly Detection in Trajectories Based on Hausdorff Distance. Proceedings of the 14th International Conference on Information Fusion (FUSION), Chicago, 5-8 July 2011, 1-8.

[25] Ten Holt, G.A., Reinders, M.J.T. and Hendriks, E.A. (2007) Multi-Dimensional Dynamic Time Warping for Gesture Recognition. Proceedings of the Conference of the Advanced School for Computing and Imaging (ASCI 2007), Heijen, 13-15 June 2007, 1-8.

[26] Laxhammar, R., Falkman, G. and Sviestins, E. (2009) Anomaly Detection in Sea Traffic—A Comparison of the Gaussian Mixture Model and the Kernel Density Estimator. Proceedings of the 12th International Conference on Information Fusion, Seattle, 6-9 July 2009, 756-763.

[27] Ristic, B., La Scala, B., Morelande, M., et al. (2008) Statistical Analysis of Motion Patterns in AIS Data: Anomaly Detection and Motion Prediction. Proceedings of the 11th International Conference on Information Fusion, Cologne, 30 June-3 July 2008, 1-7.

[28] Urban, Š., Jakob, M. and Pěchouček, M. (2010) Probabilistic Modeling of Mobile Agents’ Trajectories. In: Cao, L.B., Bazzan, A.L.C., Gorodetsky, V., Mitkas, P.A., Weiss, G. and Yu, P.S. Eds., Agents and Data Mining Interaction, Springer Berlin Heidelberg, Berlin, 59-70.

[29] Kowalska, K. and Peel, L. (2012) Maritime Anomaly Detection Using Gaussian Process Active Learning. Proceedings of the 15th International Conference on Information Fusion (FUSION), Singapore, 9-12 July 2012, 1164-1171.

[30] Will, J., Peel, L. and Claxton, C. (2011) Fast Maritime Anomaly Detection Using KD-Tree Gaussian Processes. Proceedings 2nd IMA Conference on Maths in Defence, Shrivenham, 20 October 2011, 1-7.

[31] Laxhammar, R. (2014) Conformal Anomaly Detection: Detecting Abnormal Trajectories in Surveillance Applications. University of Skövde, Skövde.

[32] De Haan, L. and Ferreira, A. (2007) Extreme Value Theory: An Introduction. Springer Science & Business Media, New York.

[33] Lee, H. and Roberts, S.J. (2008) On-Line Novelty Detection Using the Kalman Filter and Extreme Value Theory. Proceedings of the 19th International Conference on Pattern Recognition (ICPR), Tampa, 8-11 December 2008, 1-4.

[34] Korb, K.B. and Nicholson, A.E. (2010) Bayesian Artificial Intelligence. Chapman & Hall/CRC Press, London.

[35] Wong, W., Moore, A., Cooper, G. and Wagner, M. (2003) Bayesian Network Anomaly Pattern Detection for Disease Outbreaks. Proceedings of the International Conference on Machine Learning (ICML), Washington DC, 21-24 August 2003, 808-815.

[36] Cansado, A. and Soto, A. (2008) Unsupervised Anomaly Detection in Large Databases Using Bayesian Networks. Applied Artificial Intelligence, 22, 309-330.

[37] Wang, X., Lizier, J., Obst, O., Prokopenko, M. and Wang, P. (2008) Spatiotemporal Anomaly Detection in Gas Monitoring Sensor Networks. Proceedings of the 5th European Conference, EWSN 2008, Bologna, 30 January-1 February 2008, 90-105.

[38] Loy, C.C., Xiang, T. and Gong, S. (2011) Detecting and Discriminating Behavioural Anomalies. Pattern Recognition, 44, 117-132.

[39] Johansson, F. and Falkman, G. (2007) Detection of Vessel Anomalies—A Bayesian Network Approach. Proceedings of the 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, Melbourne, 3-6 December 2007, 395-400.

[40] Helldin, T. and Riveiro, M. (2009) Explanation Methods for Bayesian Networks: Review and Application to a Maritime Scenario. Proceedings of the 3rd Annual Skövde Workshop on Information Fusion Topics, Skövde, 12-13 October 2009, 11-16.

[41] Mascaro, S., Korb, K.B. and Nicholson, A.E. (2010) Learning Abnormal Vessel Behaviour from AIS Data with Bayesian Networks at Two Time Scales. Tracks A: Journal of Artists Writings, 1-34.

[42] Mascaro, S., Nicholso, A.E. and Korb, K.B. (2014) Anomaly Detection in Vessel Tracks Using Bayesian Networks. International Journal of Approximate Reasoning, 55, 84-98.

[43] Bomberger, N.A., Rhodes, B.J., Seibert, M., et al. (2006) Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness. Proceedings of the 9th International Conference on Information Fusion, Florence, 10-13 July 2006, 1-8.

[44] Rhodes, B.J., Bomberger, N.A. and Zandipour, M. (2007) Probabilistic Associative Learning of Vessel Motion Patterns at Multiple Spatial Scales for Maritime Situation Awareness. Proceedings of the 10th International Conference on Information Fusion, Québec, 9-12 July 2007, 1-8.

[45] 邱洪生. 基于卡尔曼滤波的船舶航行轨迹异常行为预测算法研究[D]: [硕士论文], 天津: 河北工业大学, 2012.

[46] Knorr, E.M., Ng, R.T. and Tucakov, V. (2000) Distance-Based Outliers: Algorithms and Applications. The VLDB Journal—The International Journal on Very Large Data Bases, 8, 237-253.

[47] Markou, M. and Singh, S. (2003) Novelty Detection: A Review—Part 1: Statistical Approaches. Signal Processing, 83, 2481-2497.

[48] Junejo, I.N., Javed, O. and Shah, M. (2004) Multi Feature Path Modeling for Video Surveillance. Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Cambridge, 23-26 August 2004, 716-719.