Abstract
The video surveillance is used to monitor ships in order to ensure safety on waterways. The ships detection is a first step in a ship automatic identification process based on video streams. The paper presents a new algorithm for ships detection on inland waterways. The algorithm must detect moving ships of all kinds, including leisure craft, that are visible on a video stream and is designed to work for stationary cameras. Furthermore, it only requires an access to video streams from existing monitoring systems without any additional hardware or special configuration of cameras. The algorithm works in variable lightning conditions and with slight changes of background. In the paper, the test application implementing the algorithm is presented together with a series of experimental results showing the algorithm quality depending on different parameters’ sets. The main purpose of the tests was to find the optimal set of twelve parameters that will become the default setting. All moving ships, including small boats and kayaks, must be detected, which is the main difference from existing solutions that mostly focus on detection of only one vessel type. In the proposed algorithm, all objects that are moving on water are detected and then non-ships are eliminated by usage of some logic rules and excluding additional image processing methods.
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References
Hyla, T., Wawrzyniak, N.: Automatic ship detection on inland waters: problems and a preliminary solution. In: Proceedings of ICONS 2019 The Fourteenth International Conference on Systems, Valencia, Spain, pp. 56–60. IARIA (2019)
Ferreira, J.C., Branquinho, J., Ferreira, P.C., Piedade, F.: Computer vision algorithms fishing vessel monitoring—identification of vessel plate number. In: De Paz, J.F., Julián, V., Villarrubia, G., Marreiros, G., Novais, P. (eds.) ISAmI 2017. AISC, vol. 615, pp. 9–17. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61118-1_2
McConnell, R.K.: Method of and apparatus for pattern recognition. US Patent 4,567,610 (1986)
Hu, W.-C., Yang, C.-Y., Huang, D.-Y.: Robust real-time ship detection and tracking for visual surveillance of cage aquaculture. J. Vis. Commun. Image Represent. 22(6), 543–556 (2011)
Szpak, Z.L., Tapamo, J.R.: Maritime surveillance: tracking ships inside a dynamic background using a fast level-set. Expert Syst. Appl. 38(6), 6669–6680 (2011)
Kaido, N., Yamamoto, S., Hashimoto, T.: Examination of automatic detection and tracking of ships on camera image in marine environment. In: 2016 Techno-Ocean, pp. 58–63 (2016)
Kim, Y.J., Chung, Y.K., Lee, B.G.: Vessel tracking vision system using a combination of Kaiman filter, Bayesian classification, and adaptive tracking algorithm. In: 16th International Conference on Advanced Communication Technology, pp. 196–201 (2014)
da Silva Moreira, R., Ebecken, N.F.F., Alves, A.S., Livernet, F., Campillo-Navetti, A.: A survey on video detection and tracking of maritime vessels. Int. J. Res. Rev. Appl. Sci. 20(1), 37–50 (2014)
Brutzer, S., Höferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: CVPR 2011, Colorado Springs, CO, USA, pp. 1937–1944 (2011)
Benezeth, Y., Jodoin, P.-M., Emile, B., Laurent, H., Rosenberger, C.: Comparative study of background subtraction algorithms. J. Electron. Imaging 19(3), 1–30 (2010)
Emgu CV Library Documentation version 3.4.3. http://www.emgu.com/wiki/files/3.4.3/document/index.html. Accessed 15 Apr 2019
KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Remagnino, P., Jones, G.A., Paragios, N., Regazzoni, C.S. (eds.) Video-Based Surveillance Systems. Springer, Boston (2002). https://doi.org/10.1007/978-1-4615-0913-4_11
Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), vol. 2, pp. 28–31. IEEE Computer Society, Washington (2004)
Godbehere, A.B., Matsukawa, A., Goldberg, K.: Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. In: 2012 American Control Conference (ACC), pp. 4305–4312 (2012)
Zeevi, S.: BackgroundSubtractorCNT Project. https://github.com/sagi-z/BackgroundSubtractorCNT. Accessed 15 Apr 2019
Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)
Guo, L., Xu, D., Qiang, Z.: Background subtraction using local SVD binary pattern. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, pp. 1159–1167 (2016)
Wawrzyniak, N., Stateczny, A.: Automatic watercraft recognition and identification on water areas covered by video monitoring as extension for sea and river traffic supervision systems. Pol. Marit. Res. 25(s1), 5–13 (2018)
Wawrzyniak, N., Hyla, T.: Automatic ship identification approach for video surveillance systems. In: Proceedings of ICONS 2019 The Fourteenth International Conference on Systems, Valencia, Spain, pp. 65–68. IARIA (2019)
Acknowledgement
This scientific research work was supported by National Centre for Research and Development (NCBR) of Poland under grant No. LIDER/17/0098/L-8/16/NCBR/2017.
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Hyla, T., Wawrzyniak, N. (2019). Ships Detection on Inland Waters Using Video Surveillance System. In: Saeed, K., Chaki, R., Janev, V. (eds) Computer Information Systems and Industrial Management. CISIM 2019. Lecture Notes in Computer Science(), vol 11703. Springer, Cham. https://doi.org/10.1007/978-3-030-28957-7_4
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