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Author Popik, Adrian
Affiliation Marine Technology Ltd. 4/6 Roszczynialskiego St., 81-521 Gdynia, Poland
E-mail a.popik@marinetechnology.pl
Author Zaniewicz, Grzegorz
Affiliation Maritime University of Szczecin, Faculty of Navigation, Institute of Geoinformatics 1–2 Wały Chrobrego St., 70-500 Szczecin, Poland
E-mail g.zaniewicz@am.szczecin.pl
Author Wawrzyniak, Natalia
Affiliation Maritime University of Szczecin, Faculty of Navigation, Institute of Geoinformatics 1–2 Wały Chrobrego St., 70-500 Szczecin, Poland
E-mail nbd.wydzial@am.szczecin.pl
ISSN printed 1733-8670
URI https://repository.am.szczecin.pl/handle/123456789/2582
Abstract Video surveillance on both marine and inland waters still only plays a mainly auxiliary role in vessel traffic observation and management. The newest technical achievements in visual systems allow camera images to be used in more sophisticated tasks, such as automatic vessel recognition and identification in observed areas. With the use of deep learning algorithms and other artificial intelligence methods, such as rough sets and fuzzy sets, new functions can be designed and implemented in monitoring systems. In this paper the challenges that were encountered and the technology that has been developed in managing video streams are presented as well as the images needed for tests and proper operation of the designed Ship Recognition and Identification System (SHREC). The current technologies, typical setups and capabilities of cameras, with regard to existing on-water video monitoring systems, are also presented. The aspects of collecting the test data in the Szczecin Water Junction area are also described. The main part of the article focuses on presenting the video data pre-processing, storing and managing procedures that have been developed for the purposes of the SHREC system.
Pages 56-63
Publisher Scientific Journals Maritime University of Szczecin, Zeszyty Naukowe Akademia Morska w Szczecinie
Keywords video surveillance
Keywords cameras
Keywords image
Keywords processing
Keywords ship identification
Keywords River Information Services
Title On-water video surveillance: data management for a ship identification system
References
  1. Bloisi, D.D., Previtali, F., Pennisi, A., Nardi, D. & Fiorini, M. (2016) Enhancing Automatic Maritime Surveillance Systems with Visual Information. IEEE Transactions on Intelligent Transportation Systems 8(4), pp. 824–833.
  2. Bobkowska, K., Przyborski, M., Kaczynska, A. & Kosiński, A. (2017) Digital Photogrammetry in the Analysis of the Ventricles’ Shape and Size. Proceedings of 2017 Baltic Geodetic Congress (BGC Geomatics 2017), June 2017, pp. 169–173.
  3. Bodus-Olkowska, I. & Uriasz, J. (2017) The Integration of Image and Nonimage Data to Obtain Underwater Situation Refinement. Proceedings of 2017 Baltic Geodetic Congress (BCG Geomatics 2017), 22–25 June 2017, Gdańsk, Poland, pp. 378–383.
  4. Hikvision (2017) A safe harbor in an ocean of threat. Smart port & maritime solution. Port & Maritime.
  5. Hyla, T. & Wawrzyniak, N. (2019) Automatic Ship Detection on Inland Waters: Problems and a Preliminary Solution. Proceedings of ICONS 2019, The Fourteenth International Conference on Systems, At Valencia, Spain, pp. 56–60.
  6. IALA (2016) IALA VTS Manual, Edition 6. International Association of Marine Aids to Navigation and Lighthouse Authorities, 2016.
  7. IMO (1974) SOLAS International Convention for the Safety of Life at Sea. International Maritime Organisation.
  8. Kazimierski, W. & Zaniewicz, G. (2018) The Concept of Anti-Collision System for Underwater Vehicles Based on Forward Looking Sonar. Proceedings of 2018 Baltic Geodetic Congress (BGC Geomatics 2018), pp. 321–327.
  9. Lubczonek, J. & Wlodarczyk-Sielicka, M. (2018) The Use of an Artificial Neural Network for a Sea Bottom Modelling. In: Damaševičius R., Vasiljevienė G. (Eds) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science, vol. 920. Springer, pp. 357–369.
  10. Möller, D.P., Jehle, I.A., Froese, J., Deutschmann, A. & Koch, T. (2018) Securing Maritime Traffic Management. Proceedings of 2018 IEEE International Conference on Electro/Information Technology (EIT), pp. 0453–0458.
  11. Połap, D. (2018) Model of identity verification support system based on voice and image samples. Journal of Universal Computer Science 24(4), pp. 460–474.
  12. Połap, D., Woźniak, M., Wei, W., & Damaševičius, R. (2018) Multi-threaded learning control mechanism for neural networks. Future Generation Computer Systems 87, pp. 16–34.
  13. Stateczny, A. (2017) Sensors in River Information Services of the Odra River in Poland: Current State and Planned Extension. Proceedings of 2017 Baltic Geodetic Congress (BGC Geomatics 2017), June 2017, pp. 301–306.
  14. Stateczny, A., Gronska, D. & Motyl, W. (2018) Hydrodron – new step for profesional hydrography for restricted waters. Proceedings of 2018 Baltic Geodestic Congress (BGC Geomatics 2018), June 2018, pp. 226–230.
  15. Wawrzyniak, N. & Hyla, T. (2019) Automatic Ship Identification Approach for Video Surveillance Systems. Proceedings of ICONS 2019 The Fourteenth International Conference on Systems, At Valencia, Spain, pp. 65–68.
  16. Wawrzyniak, N. & Stateczny, A. (2018) Automatic watercraft recognition and identification on water areas covered by video monitoring as extension for sea and river traffic supervision systems. Polish Maritime Research 25, s1, pp. 5–13.
ISSN on-line 2392-0378
Language English
Funding No data
Figures 6
Tables 0
DOI 10.17402/372
Published 2019-12-27
Accepted 2019-10-03
Recieved 2019-05-06


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