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Route tracking for self-propelled ship model

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Abstract

To ensure the safety of the navigation, self-propelled ship model test is widely used in navigable administer engineering to visually and really reflect navigable condition, offering reasonable suggestions for the design of route. This paper proposed a video analysis based route tracking approach for self-propelled ship model that sails in large scale river models, which can realize automatic measurement for ship model motion parameters. Firstly, the captured videos of the self-propelled ship model are transferred to the computer via wireless local area network (WLAN). Then, the camera lens distortion is eliminated by rectify and aerial view is reconstructed. Third, ORB and binary BoF classifier are used to detect ship model. At last, through frame difference and Freeman chain-code, the coordinates of ship model’s markers can be obtained. Moreover, on visual interactive interface, the route of the ship model is plotted, and velocities and drift angles of ship model are also calculated. This approach was tested on the several river models, such as Jianzishan navigation junction, which is a medium-sized water conservancy project located in the middle reaches of the Minjiang River. The results from the experiments demonstrate the approach can not only realize accurate measurement of coordinates of ship model, but also can visually map out the route of the ship model that sailing in large scale river models.

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References

  1. Cai XY, Cai RZ (2017) Test research of influence of Xiangjiaba unsteady flow on downstream channel navigation condition. Port Waterway Eng 2017(2):77–82

    Google Scholar 

  2. Cai C, Chen L, Cai RZ, Zheng T (2009) Ship model testing of navigation for Fujin dam key project in Fujiang. Port Engineering Technology 46(1):12–14

    Google Scholar 

  3. Cai XY, Cai C, Lei PH (2017) Application for design optimization of navigation hub based on small scale ship model technique. Journal of Chongqing Jiao Tong University (Natural Science) 36(8):58–62

    Google Scholar 

  4. Carsten S, Markus U, Christian W (2008) Machine vision algorithms and applications. Wiley-VCH, Berlin, pp 32–36

    Google Scholar 

  5. Chang X, Nie F, Wang S, Yang Y, Zhou X, Zhang C (2016) Compound rank-k projections for bilinear analysis. IEEE Trans Neural Netw Learn Syst 27(7):1502–1513

    Article  MathSciNet  Google Scholar 

  6. Chang X, Yu YL, Yang Y, Xing EP (2017) Semantic pooling for complex event analysis in untrimmed videos. IEEE Trans Pattern Anal Mach Intell 39(8):1617–1632

    Article  Google Scholar 

  7. Chang X, Ma Z, Yang Y, Zeng Z, Hauptmann AG (2017) Bi-level semantic representation analysis for multimedia event detection. IEEE Trans Cybern 47(5):1180–1197

    Article  Google Scholar 

  8. Chang X, Ma Z, Lin M, Yang Y, Hauptmann AG (2017) Feature interaction augmented sparse learning for fast Kinect motion detection. IEEE Trans Image Process 26(8):3911–3920

    Article  MathSciNet  Google Scholar 

  9. Chen ZZ, Bai XG, Wu SG, Xian YJ, Chen L (2013) Position tracking of model ships based on computer vision. Journal of Shanghai Maritime University 34(03):27–31

  10. Choi BD, Han JW, Kim CS (2006) Frame rate up-conversion using perspective transform. IEEE Trans Consum Electron 52(3):975–982

    Article  Google Scholar 

  11. Daniel SC, Luis G, Miguel AF, Agustín S (2017) Automatic correction of perspective and optical distortions. Comput Vis Image Underst 161(C):1–10

  12. Gao ZR, Zhang QC, Su Y, Wu SQ (2017) Accuracy evaluation of optical distortion calibration by digital image correlation. Opt Lasers Eng 98:143–152

    Article  Google Scholar 

  13. Hou YX, Zhao B, Zhang HG, Yan HB (2013) A fast image spam filter based on ORB. IEEE International Conference on Network Infrastructure & Digital Content, Beijing, pp 503–507

    Google Scholar 

  14. Joseph T, Patel ND, Swain AK (2015) Real-time object tracking based on colour feature and perspective projection. International Conference on Sensing Technology, Sydney, pp 665–670

    Google Scholar 

  15. Junji O, Tsukasa O (2011) Contour pattern recognition through auditory labels of freeman chain codes for people with visual impairments. IEEE Int Conf Syst 32(14):1088–1093

    Google Scholar 

  16. Li JH (2008) Application of self- propelled ship model for research on navigation condition at port entrance area. Port & Waterway Engineering 2008(06):117–121

  17. Li YB, Wang YL (2004) Application of technique of ship model navigation test in study on water transport engineering. J Waterw Harb 25(S1):8–13

  18. Li J, Guo SA, Ye F (2011) Shape recognition based on freeman chain code. Adv Mater Res (317–319): 2490–2496

  19. Li ZH, Nie FP, Chang XJ, Yang Y (2017) Beyond trace ratio: weighted harmonic mean of trace ratios for multiclass discriminant analysis. IEEE Trans Knowl Data Eng 29(10):2100–2110

    Article  Google Scholar 

  20. Liu L, Wiliem A, Chen S, Lovell BC (2014) Automatic image attribute selection for zero-shot learning of object categories. In: International Conference on Pattern Recognition, Stockholm, Sweden, 24-28 Aug. pp 2619–2624

  21. Ni SL (2000) Experimental method of navigable ship model and its application in engineering. Journal of SSSRI 23(02):91–10

  22. Radu AC, Carmen MS (2014) Industrial applications of image processing. Acta Universitatis Cibiniensis 64(1):17–21

    Article  Google Scholar 

  23. Rafael MS, Eugenio A (2008) A multiple object tracking approach that combines colour and depth information using a confidence measure. Pattern Recogn Lett 29(10):1504–1514

    Article  Google Scholar 

  24. Sun Q, Wang XY, Xu JP (2016) Camera self-calibration with lens distortion. Int J Light Electron Optics 127(10):4506–4513

    Article  Google Scholar 

  25. Wang YC, Cai RZ, Li XB (2004) Navigable application of small size self-sailing boat models. Journal of Scientific Instrument 25(4):993–994

  26. Wang SG, Wang XJ, Chen HX (2008) A stereo video segmentation algorithm combining disparity map and frame difference. International Conference on Intelligent System and Knowledge Engineering. pp 1121–1124

  27. Xu GY, Zhu YJ, Guo XM, Hu BL, Sun T, Liang WB (1986) A laser ship model track-meter. Ship Building of China 1986(03):82–90

  28. Yang F, Lu H, Zhang W, Yang G (2012) Visual tracking via bag of features. IET Image Process 6(2):115–128

    Article  MathSciNet  Google Scholar 

  29. Zeng F (2017) Experimental study on navigable condition by ship model in waterway regulation for Dongxikou reach of the Yangtze River. Port Waterway Eng 2017(2):71–76

    Google Scholar 

  30. Zeng ZQ, Li ZH, Cheng D, Zhang HX, Zhan K, Yang Y (2017) Two-stream multi-rate recurrent neural network for video-based pedestrian re-identification. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2017.2767557

  31. Zhang CH, Duan XH, Xu SY, Song Z, Luo M (2007) An improved moving object detection algorithm based on frame difference and edge detection. International Conference on Image & Graphics, Chengdu, pp 519–523

    Google Scholar 

  32. Zhang T, Zhang FM, Qu XH, Zhi GT, Liu BD, An WN (2015) 2D laser scanner based trajectory measurement system for sailing ship model. Journal of Electronic Measurement and Instrument 29(04):531–541

  33. Zhang T, Liu L, Wiliem A, Lovell B (2016) Is Alice chasing or being chased?: determining subject and object of activities in videos. In: IEEE Winter Conference on Applications of Computer Vision, Lake Placid, USA, 7-9 Mar, pp 1–71

Download references

Acknowledgments

This research was supported by the Key Research and Development Projects in Chongqing (cstc2017rgzn-zdyfX0025), the Chongqing Postdoctoral Science Foundation (Xm2015014), the Opening Fund of Key Laboratory of Inland Waterway Regulation Engineering (Chongqing Jiaotong University), Ministry of Communications (NHHD-201503), the Shandong Provincial Natural Science Foundation, China (ZR2016FQ25), and the Visiting Scholar Foundation of Key Laboratory of Optoelectronic Technology & Systems (Chongqing University), Ministry of Education.

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Authors and Affiliations

  1. Key Laboratory for Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing, 400044, China

    Zhenghao Li & Yangaizhu Zhou

  2. Key Laboratory of Inland Waterway Regulation Engineering of Ministry of Communications, Chongqing Jiao Tong University, Chongqing, 400016, China

    Zhenghao Li, Jun Wu & Yuejie Shu

  3. Chongqing Academy of Science and Technology, Chongqing, 401123, China

    Peng Han

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  1. Zhenghao Li
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  2. Yangaizhu Zhou
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Correspondence to Zhenghao Li.

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Li, Z., Zhou, Y., Wu, J. et al. Route tracking for self-propelled ship model. Multimed Tools Appl 78, 4365–4379 (2019). https://doi.org/10.1007/s11042-018-5751-9

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  • DOI: https://doi.org/10.1007/s11042-018-5751-9

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