Open data for anomaly detection in maritime surveillance

被引:43
|
作者
Kazemi, Samira [1 ]
Abghari, Shahrooz [1 ]
Lavesson, Niklas [1 ]
Johnson, Henric [1 ]
Ryman, Peter
机构
[1] Blekinge Inst Technol, Sch Comp, SE-37179 Karlskrona, Sweden
关键词
Open data; Anomaly detection; Maritime security; Maritime domain awareness;
D O I
10.1016/j.eswa.2013.04.029
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Maritime surveillance has received increased attention from a civilian perspective in recent years. Anomaly detection is one of many techniques available for improving the safety and security in this domain. Maritime authorities use confidential data sources for monitoring the maritime activities; however, a paradigm shift on the Internet has created new open sources of data. We investigate the potential of using open data as a complementary resource for anomaly detection in maritime surveillance. We present and evaluate a decision support system based on open data and expert rules for this purpose. We conduct a case study in which experts from the Swedish coastguard participate to conduct a real-world validation of the system. We conclude that the exploitation of open data as a complementary resource is feasible since our results indicate improvements in the efficiency and effectiveness of the existing surveillance systems by increasing the accuracy and covering unseen aspects of maritime activities. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:5719 / 5729
页数:11
相关论文
共 50 条
  • [31] Anomaly Detection and Modeling of Surveillance Video
    Yang F.
    Xiao B.
    Yu Z.
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2021, 58 (12): : 2708 - 2723
  • [32] Anomaly detection in surveillance videos: A survey
    Wang Z.
    Zhang Y.
    Qinghua Daxue Xuebao/Journal of Tsinghua University, 2020, 60 (06): : 518 - 529
  • [33] A Review of Anomaly Detection in Automated Surveillance
    Sodemann, Angela A.
    Ross, Matthew P.
    Borghetti, Brett J.
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 2012, 42 (06): : 1257 - 1272
  • [34] Anomaly detection for video surveillance applications
    Au, Carmen E.
    Skaff, Sandra
    Clark, James J.
    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 4, PROCEEDINGS, 2006, : 888 - +
  • [35] Anomaly Detection in Endemic Disease Surveillance Data Using Machine Learning Techniques
    Eze, Peter U.
    Geard, Nicholas
    Mueller, Ivo
    Chades, Iadine
    HEALTHCARE, 2023, 11 (13)
  • [36] Video Anomaly Detection Using Open Data Filter and Domain Adaptation
    Zhang, Chen
    Li, Guorong
    Su, Li
    Zhang, Weigang
    Huang, Qingming
    2020 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), 2020, : 395 - 398
  • [37] Anomaly Detection in Public Procurements using the Open Contracting Data Standard
    Niessen, Maria Elisabeth Kehler
    Paciello, Julio Manuel
    Fernandez, Juan Ignacio Pane
    2020 SEVENTH INTERNATIONAL CONFERENCE ON EDEMOCRACY & EGOVERNMENT (ICEDEG), 2020, : 127 - 134
  • [38] AUTOMATIC MARITIME SURVEILLANCE WITH VISUAL TARGET DETECTION
    Bloisi, Domenico
    Iocchi, Luca
    Fiorini, Michele
    Graziano, Giovanni
    INTERNATIONAL DEFENSE AND HOMELAND SECURITY SIMULATION WORKSHOP, (DHSS 2011), 2011, : 141 - 145
  • [39] Target Detection and Tracking in Maritime Surveillance Mission
    Sabordo, Madeleine G.
    Aboutanios, Elias
    MODELING AND SIMULATION FOR DEFENSE SYSTEMS AND APPLICATIONS X, 2015, 9478
  • [40] Vision based boat detection for maritime surveillance
    Thanh-Hai Tran
    Thi-Lan Le
    2016 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATIONS (ICEIC), 2016,