Automatic watercraft recognition and identification on water areas covered by video monitoring as extension for sea and river traffic supervision systems

• Autorzy: Wawrzyniak N. , Stateczny A.
• Języki publikacji: EN

Abstrakty

EN

The article presents the watercraft recognition and identification system as an extension for the presently used visual water area monitoring systems, such as VTS (Vessel Traffic Service) or RIS (River Information Service). The watercraft identification systems (AIS - Automatic Identification Systems) which are presently used in both sea and inland navigation require purchase and installation of relatively expensive transceivers on ships, the presence of which is not formally required as equipment of unconventional watercrafts, such as yachts, motor boats, and other pleasure crafts. These watercrafts may pose navigation or even terrorist threat, can be the object of interest of the customs, or simply cause traffic problems on restricted water areas. The article proposes extending the traffic supervision system by a module which will identify unconventional crafts based on video monitoring. Recognition and identification will be possible through the use of image identification and processing methods based on artificial intelligence algorithms, among other tools. The system will be implemented as independent service making use of the potential of SOA (Service Oriented Architecture) and XML/SOAP (Extensible Markup Language/Simple Object Access Protocol) technology

• Wydawca: -
• Czasopismo: Polish Maritime Research
• Rocznik: 2018
• Tom: 25
• Numer: Special Issue S1
• Opis fizyczny: p.5-13,fig.,ref.

Twórcy

autor

Wawrzyniak N.

• Marine Technology Ltd., Cyfrowa 6, B.3.04a, 71-441 Szczecin, Poland

autor

Stateczny A.

• Faculty of Ocean Engineering and Ship Technology, Gdansk University of Technology, 11/12 Narutowicza St., 80-233 Gdansk, Poland

Bibliografia

• 1. IALA Recommendation V – 128 on Operational and Technical Performance Requirements for. VTS Equipment Ed. 2.0, 2005.
• 2. PIANC RIS Guidelines and Recommendations for River Information Systems, 2011
• 3. Zajac, P.: Evaluation of Automatic Identification Systems According to ISO 50001: 2011, Progress in Automation, Robotics and Measuring Techniques: Control and Automation, Book Series: Advances in Intelligent Systems and Computing vol. 350, pp: 345-355, 2015.
• 4. Kubik, T.: GIS – Network solutions (in Polish), PWN, 2009
• 5. Stateczny, A.: Full Implementation of the River Information Services of Border and Lower Section of the Oder in Poland. Proceedings of Baltic Geodesy Congress, Gdansk, Poland, 2016.
• 6. Wlodarczyk-Sielicka M., Wawrzyniak N.: Problem of Bathymetric Big Data Interpolation for Inland Mobile Navigation System. In: Damaševičius R., Mikašytė V. (eds) Information and Software Technologies. ICIST 2017. Communications in Computer and Information Science, vol 756, pp 611-621. Springer, Cham,
• 7. Kazimierski, W., Stateczny, A.: Radar and Automatic Identification System track fusion in an Electronic Chart Display and Information System. Journal of Navigation, volume: 68, issue: 6, pp: 1141-1154, 2015.
• 8. International Maritime Organisation, SOLAS International Convention for the Safety of Life at Sea, 1974.
• 9. Jines, SP., Dwyer, DM., Lewis, MB.: The utility of multiple synthesized views in the recognition of unfamiliar faces, Quarterly Journal of Experimental Psychology, vol. 70, issue 5, pp 906-918, 2017.
• 10. Choi, K., Jeong, Y., Gil, J.: Design and implementation of P2P home monitoring system architecture with IP cameras for a vacuum robot in ubiquitous environments, International Journal of Sensors Network, vol.20, pp 166176, 2016.
• 11. Yuksel, G, Yalituna, B., Tartar, O., Yoruk, O.: Ship recognition and classification using silhouettes extracted from optical images, In Proceedings of Signal Processing and Communication Application Conference (SIU), IEEE 2016.
• 12. Akinlar, C., Topal,: EDCircles: A real-time circle detector with a false detection control, Pattern Recognit., vol. 46, no. 3, pp. 725–740, 2013.
• 13. Zhang, H., Wiklund, K., Andersson, M.: A fast and robust circle detection method using isosceles triangles sampling, Pattern Recognition, vol. 54, pp. 218–228, 2015.
• 14. Barata, C., Ruela, M., Francisco, M., Mendonca, T. & Marques, J. S.: Two systems for the detection of melanomas in dermoscopy images using texture and color features, IEEE Syst. Journal., vol. 8, no. 3, pp. 965–979, 2014.
• 15. Hu, P., Wang, W., Zhang, C. & Lu, K.: Detecting Salient Objects via Color and Texture Compactness Hypotheses, IEEE Trans. Image Process., vol. 25, no. 10, pp. 4653–4664, 2016.
• 16. Chaudhary, P., Sharma, S.: A Color, Texture and Shape Based Hybrid Approach for Clothing Retrieval Techniques, IJMCA, vol. 6, no. 4, pp. 382–387, 2016.
• 17. Kadir, A., Nugroho, L., Susanto, A. & Santosa, P.: Leaf Classification Using Shape, Color, and Texture Features, Int. J. Comput. Trends Technol., pp. 225–230, 2011.
• 18. Wawrzyniak, N., Stateczny, A.: MSIS Image Positioning in Port Areas with the Aid of Comparative Navigation Methods. Polish Maritime Research, vol. 24, Issue. 1, pp. 32-41, 2017.
• 19. Viola, P., Jones M.: Rapid object detection using a boosted cascade of simple features, Computer Vision Pattern Recognition, vol. 1, p. I--511-I-518, 2001.
• 20. Wen, X., Shao, L., Fang, W. & Xue, Y.: Efficient feature selection and classification for vehicle detection, IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 3, pp. 508–517, 2015.
• 21. Wen, X., Shao, L., Xue, Y., & Fang, W.: A rapid learning algorithm for vehicle classification, Inf. Sci. (Ny)., vol. 295, pp. 395–406, 2015.
• 22. Feineigle, P.A., Morris, D.D., Snyder, F.D.: Ship Recognition Using Optical Imagery for Harbor Surveillance, Proceedings of Association for Unmanned Vehicle Systems International (AUVSI), Washington DC, 2007.
• 23. Tang, J., Deng, C., Huang, G., & Zhao, B.: CompressedDomain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine, IEEE Trans. Geosci. Remote Sens., vol. 53, no. 3, pp. 1174–1185, 2015.
• 24. Zou, Z., Shi, Z.: Ship detection in spaceborne optical image with SVD networks, vol. 54, no. 10, pp. 5832–5845, 2016.
• 25. Stateczny, A.: Sensors in River Information Services of the Odra River in Poland: Current State and Planned Extension. Proceedings of Baltic Geodesy Congress, Gdansk, Poland, 2017.
• 26. Stateczny, A., Lubczonek, J., Kantak T.: Radar Sensors Planning for the Purpose of Extension of River Information Services in Poland. Proceedings of 16th International Radar Symposium (IRS), International Radar Symposium Proceedings, H. Rohling (Ed.), Dresden, Germany, 2015.
• 27. IALA Recommendation V-145 on the Inter-VTS Exchange Format (IVEF) Service, Edition 1.0, 2011.
• 28. Kazimierski, W., Stateczny, A.: Fusion of Data from AIS and Tracking Radar for the Needs of ECDIS. Signal Processing Symposium, Jachranka, 2013.
• 29. Kazimierski, W., Wawrzyniak, N.: Exchange of navigational information between VTS and RIS for inland shipping user needs, in Mikulski J.(ed.) Telematics in the Transport Environment, Book Series: CCIS vol.471, pp. 294-303, 2014.
• 30. Galor, W.: Sea-river shipping in Polish inland water, Scientific Journal of Maritime University of Szczecin, vol. 50 (122), pp.  84–90, 2017.
• 31. IRIS 2 Europe, Implementation of River Information Services in Europe, Technical concept for RIS data exchange (part of R2D2), SuAc 3.4, 2010.
• 32. Kazimierski, W., Zaniewicz, G., Olkowska, I.: Integrated presentation of navigational data in a mobile navigation system for inland waters with the use of HUD, Scientific Journal of Maritime University of Szczecin, vol. 49 (121), pp.  84–92, 2017.
• 33. Wlodarczyk-Sielicka, M.: Importance of neighbourhood parameters during clustering of bathymetric data using neural network. G. Dregvaite and R. Damasevicius (Eds.): ICIST 2016, Communications in Computer and Information Science 639: Information and Software Technologies, pp 441-452, 2016,
• 34. Kedzierski, M., Wierzbicki, D.: Methodology of improvement of radiometric quality of images acquired from low altitudes, Measurement, Vol. 92, pp. 70-78, 2016

Identyfikatory

DOI

10.2478/pomr-2018-0016