1. bookVolumen 23 (2022): Edición 1 (February 2022)
Detalles de la revista
Primera edición
20 Mar 2000
Calendario de la edición
4 veces al año
Acceso abierto

Method of Improving Incomplete Spatial-Temporal Data in Inland Navigation, on the Basis of Industrial Camera Images – West Oder River Case Study

Publicado en línea: 18 Feb 2022
Volumen & Edición: Volumen 23 (2022) - Edición 1 (February 2022)
Páginas: 48 - 61
Detalles de la revista
Primera edición
20 Mar 2000
Calendario de la edición
4 veces al año

1. Ali, A. T. and Dagless, E. L. (1992) A parallel processing model for real-time computer vision-aided road traffic monitoring, Parallel Processing Letters, 2, 257–264.10.1142/S0129626492000398 Search in Google Scholar

2. Allgeuer, P. and Behnke, S. (2018) Fused Angles and the Deficiencies of Euler Angles. In: IEEE International Conference on Intelligent Robots and Systems. doi: 10.1109/IROS.2018.8593384.10.1109/IROS.2018.8593384 Search in Google Scholar

3. Andrei, C. (2006) 3D affine coordinate transformations, Geometria. Search in Google Scholar

4. Anitha, J. J. and Deepa, S. M. (2014) Tracking and Recognition of Objects using SURF Descriptor and Harris Corner Detection, International Journal of Current Engineering and Technology, 4(2), 775–778. Search in Google Scholar

5. Baetslé, P.-L. (1966) Conformal transformations in three dimensions, Photogrammetric engineering and remote sensing. Search in Google Scholar

6. Banachowicz, A. et al. (2008) Badania dostępności systemu DGPS na dolnej Odrze, Czasopismo Techniczne Politechniki Krakowskiej. Search in Google Scholar

7. Brazeal, R. (2013) Three dimensional coordinate transformations for registering terrestrial laser scanning datasets based on tie points, (SUR 6905-Point Cloud Analysis). doi: 10.13140/2.1.1993.9204. Search in Google Scholar

8. Brazetti, L. and Scaioni, M. (2009) Automatic orientation of image sequences for 3D object reconstruction: first results of a method integrating photogrammetric and computer vision algorithms, International Archives of Photogrammetry, Remote Sensing, XXXVIII(5/W1). Search in Google Scholar

9. Cao, X. et al. (2020) Ship recognition method combined with image segmentation and deep learning feature extraction in video surveillance, Multimedia Tools and Applications. Springer, 79(13–14), 9177–9192. doi: 10.1007/s11042-018-7138-3.10.1007/s11042-018-7138-3 Search in Google Scholar

10. Chen, Y., Shen, Y. and Liu, D. (2004) Simplified model of three dimensional-datum transformation adapted to big rotation angle, Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University. Search in Google Scholar

11. Colomina, I. and Molina, P. (2014) Unmanned aerial systems for photogrammetry and remote sensing : A review, ISPRS Journal of Photogrammetry and Remote Sensing. International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS), 92, 79–97. doi: 10.1016/j.isprsjprs.2014. Search in Google Scholar

12. Deakin, R. (2006) A note on the Bursa-Wolf and Molodensky-Badekas transformations, (May 2006), 1–21. Search in Google Scholar

13. Deakin, R. E. (1998) 3-D coordinate transformations, Surveying and Land Information Systems. doi: 10.1201/9781315108858-16.10.1201/9781315108858-16 Search in Google Scholar

14. Dickinson, K. W. and Waterfall, R. C. (1984) Video image processing for monitoring road traffic. In: IEE Conference Publication, 105–109. Search in Google Scholar

15. Diebel, J. (2006) Representing attitude: Euler angles, unit quaternions, and rotation vectors, Matrix. doi: 10.1093/jxb/erm298.10.1093/jxb/erm29818182420 Search in Google Scholar

16. El-Ashmawy, K. L. A. (2015) A comparison between analytical aerial photogrammetry, laser scanning, total station and global positioning system surveys for generation of digital terrain model, Geocarto International. doi: 10.1080/10106049.2014.883438.10.1080/10106049.2014.883438 Search in Google Scholar

17. Europe, E. C. for and COMMITTEE, I. T. (2005) Guidelines and recommendations for river information services. Geneva. Search in Google Scholar

18. European Commisin (2007) Rozporządzenie Komisji Wspólnoty Europejskiej. Poland. Search in Google Scholar

19. Frost, D. and Tapamo, J. (2013) Detection and tracking of moving objects in a maritime environment using level set with shape priors, EURASIP Journal on Image Video Processing, 1–16.10.1186/1687-5281-2013-42 Search in Google Scholar

20. Grewal, M. S., Weill, L. R. and Andrews, A. P. (2006) Global Positioning Systems, Inertial Navigation, and Integration, Second Edition, Global Positioning Systems, Inertial Navigation, and Integration, Second Edition. doi: 10.1002/9780470099728. Search in Google Scholar

21. Huang, J. and You, S. (2012) Point cloud matching based on 3D self-similarity. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. doi: 10.1109/CVPRW.2012.6238913.10.1109/CVPRW.2012.6238913 Search in Google Scholar

22. Jue, L. (2008) Research on close-range photogrammetry with big rotation angle, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII(Part B6b), 11–14. Search in Google Scholar

23. Kandil, H. and Atwan, A. (2012) A Comparative Study between SIFT- Particle and SURF-Particle Video Tracking Algorithms, International Journal of Signal Processing, Image Processing and Pattern Recognition, 5(3), 111–122. Search in Google Scholar

24. Kolecki, J. et al. (2020) Calibration of industrial cameras for aerial photogrammetric mapping, Remote Sensing. doi: 10.3390/RS12193130.10.3390/rs12193130 Search in Google Scholar

25. Kujawski, A. (2015) Inland waterway vessels tracking using Closed Circuit Television Video, Scientific Journals of the Maritime University of Szczecin, 44(116), 135–140. Search in Google Scholar

26. Kujawski, A. and Stępień, G. (2017) A method of determining inland vessel position using a single stationary, non-metric camera, Scientific Journals of the Maritime University of Szczecin, 52(124), 103–111. doi: 10.17402/251. Search in Google Scholar

27. Li, R., Liu, C. and School, F. N. (2013) An Object Tracking Algorithm Based on Global SURF Feature, Journal of Information and Computational Science, 10(7), 2159–2167. doi: 10.12733/jics20101694.10.12733/jics20101694 Search in Google Scholar

28. Luhmann, T. et al. (2014) Close-range photogrammetry and 3D imaging. 2nd edn. Berlin/Boston: Walter de Gruyter.10.1515/9783110302783 Search in Google Scholar

29. Maddalena, L. and Petrosino, A. (2008) A self-organizing approach to background subtraction for visual surveillance applications, IEEE Transactions on Image Processing, 17(7), 1168–1177.10.1109/TIP.2008.92428518586624 Search in Google Scholar

30. Mataija, M., Pogarčić, M. and Pogarčić, I. (2014) Helmert Transformation of Reference Coordinating Systems for Geodesic Purposes in Local Frames, Procedia Engineering. Elsevier B.V., 69, 168–176. doi: 10.1016/j.proeng.2014. Search in Google Scholar

31. Matthies, L., Kanade, T. and Szeliski, R. (1989) Kalman filter-based algorithms for estimating depth from image sequences, International Journal of Computer Vision, 3, 209–238. doi: 10.1007/BF00133032.10.1007/BF00133032 Search in Google Scholar

32. Moreira, R. D. S. et al. (2014) A survey on video detection and tracking of maritime vessels, IJRRAS, 20(July), 37–50. Search in Google Scholar

33. Nie, X., Yang, M. and Liu, R. W. (2019) Deep Neural Network-Based Robust Ship Detection Under Different Weather Conditions, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. IEEE, 47–52. doi: 10.1109/ITSC.2019.8917475.10.1109/ITSC.2019.8917475 Search in Google Scholar

34. Pamuła, W. (2014) Detection of vehicles in a video stream using spatial frequency domain features, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).10.1007/978-3-319-11331-9_59 Search in Google Scholar

35. Parliament, T. H. E. E. et al. (2005) Directive 2005/44/EC of the European Parliament and of the Council of 7 september 2005, Official Journal of the European Union, 2005(1882), 12–25. Search in Google Scholar

36. Ruffhead, A. C. (2020) Equivalence properties of 3D conformal transformations and their application to reverse transformations, Survey Review. doi: 10.1080/00396265.2019.1708604.10.1080/00396265.2019.1708604 Search in Google Scholar

37. Sawhney, H. S. (1994) 3D geometry from planar parallax. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. doi: 10.1109/cvpr.1994.323927.10.1109/CVPR.1994.323927 Search in Google Scholar

38. Seedahmed, G. and Habib, A. (2002) Linear recovery of the exterior orientation parameters in a planar object space, (The Ohio State University). Search in Google Scholar

39. Späth, H. (2004) A Numerical Method for Determining the Spatial HELMERT Transformation in the Case of Different Scale Factors, Fachbeiträge, 4(6), 255–257. Search in Google Scholar

40. Specht, C., Specht, M. and Dąbrowski, P. S. (2019) Polish DGPS system: 1995-2018 – studies of reference station operating zones, TransNav, 13(3), 581–586. doi: 10.12716/1001. Search in Google Scholar

41. Stępień, G. (2018) Method of the Determination of Exterior Orientation of Sensors in Hilbert Type Space, Sensors, 18(3), 891. doi: 10.3390/s18030891.10.3390/s18030891587712529562598 Search in Google Scholar

42. Stępień, G. et al. (2020) Dimensioning method of floating offshore objects by means of quasi-similarity transformation with reduced tolerance errors, Sensors (Switzerland). doi: 10.3390/s20226497.10.3390/s20226497770904333203050 Search in Google Scholar

43. Titterton, D. and Weston, J. (2004) Strapdown inertial navigation technology, Second Edition, The Institution of Electronical Engineers, Reston USA.10.1049/PBRA017E Search in Google Scholar

44. Wang, N., Wang, Y. and Er, M. J. (2020) Review on deep learning techniques for marine object recognition: Architectures and algorithms, Control Engineering Practice. Elsevier Ltd, (December 2019), 104458. doi: 10.1016/j.conengprac.2020.104458.10.1016/j.conengprac.2020.104458 Search in Google Scholar

45. Wawrzyniak, N., Hyla, T. and Popik, A. (2019) Vessel Detection and Tracking Method Based on Video Surveillance, Sensors, 19(5230), 1–14. Search in Google Scholar

46. Wawrzyniak, N. and 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(97), 5–13.10.2478/pomr-2018-0016 Search in Google Scholar

47. Wigan, M. R. and Cullinan, M. (1984) MACHINE VISION AND ROAD RESEARCH: NEW TASKS, OLD PROBLEMS. In: Proceedings - Conference of the Australian Road Research Board. Search in Google Scholar

48. Wigan, M R and Cullinan, M. C. (1984) Digital image processing: an applications review for road research applications. In: Australasian Conference on Computer Graphics, 2nd, 1984, Melbourne, Australia (AusGraph). Search in Google Scholar

49. Wisniewski, B., Bruniecki, K. and Moszynski, M. (2013) Evaluation of RTKLIB’s Positioning Accuracy Using low-cost GNSS Receiver and ASG-EUPOS, TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 7(2), 79–85. doi: 10.12716/1001. Search in Google Scholar

50. Zhang, D. (2010) Exploitation of photogrammetry measurement system, Optical Engineering. doi: 10.1117/1.3364057.10.1117/1.3364057 Search in Google Scholar

51. Zhou, S. et al. (2021) Integrating computer vision and traffic modeling for near-real-time signal timing optimization of multiple intersections, Sustainable Cities and Society. Elsevier Ltd, 68(September 2020), 102775. Search in Google Scholar

Artículos recomendados de Trend MD

Planifique su conferencia remota con Sciendo