Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-23T07:25:55.865Z Has data issue: false hasContentIssue false
  • English
  • Français

Verification of AIS Data by using Video Images taken by a UAV

Published online by Cambridge University Press:  08 May 2019

Fan Zhou ,
Shengda Pan  and
Jingjing Jiang
Fan Zhou*
Affiliation:
(College of Information Engineering, Shanghai Maritime University)
Shengda Pan
Affiliation:
(College of Information Engineering, Shanghai Maritime University)
Jingjing Jiang
Affiliation:
(College of Information Engineering, Shanghai Maritime University)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Effective technical methods for verifying the authenticity and accuracy of Automatic Identification System (AIS) data, which are important for safe navigation and traffic regulation, are still lacking. In this study, we propose a new method to verify AIS data by using video images taken by an Unmanned Aerial Vehicle (UAV). An improved ViBe algorithm is used to extract the ship target image from the video images and the ship's spatial position is calculated using a monocular target-positioning algorithm. The positioning results are compared with the position, speed and course data of the same ship in AIS, and the authenticity and accuracy of the AIS data are verified. The results of the experiment conducted in the inland waterways of Huangpu River in Shanghai, China, show that AIS signals can be automatically checked and verified by a UAV in real time and can thus improve the supervision efficiency of maritime departments.

Keywords

Automatic Identification SystemUnmanned Aerial VehicleImage ProcessingSpatial Position MeasurementPrecision Analysis
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 6 , November 2019 , pp. 1345 - 1358
Copyright
Copyright © The Royal Institute of Navigation 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Altan, Y.C. and Otay, E.N. (2018). Spatial mapping of encounter probability in congested waterways using AIS. Ocean Engineering, 164, 263–271. doi:10.1016/j.oceaneng.2018.06.049Google Scholar
Barnich, O. and Van Droogenbroeck, M. (2011). ViBe: A Universal Background Subtraction Algorithm for Video Sequences. IEEE Transactions on Image Processing, 20(6), 1709–1724. doi:10.1109/tip.2010.2101613Google Scholar
Chen, H., Kirubarajan, T. and Bar-Shalom, Y. (2003). Performance Limits of Track-to-Track Fusion Versus Centralized Estimation: Theory and Application. IEEE Transactions on Aerospace and Electronic Systems, 39(2), 386–400. doi:10.1109/taes.2003.1207252Google Scholar
Daronkolaei, A.G., Nazari, V., Menhaj, M.B. and Shiry, S. (2008). A Joint Probability Data Association Filter Algorithm for Multiple Robot Tracking Problems. Tools in Artificial Intelligence. InTech, 163–186. doi:10.5772/74Google Scholar
Goerlandt, F. and Kujala, P. (2011). Traffic Simulation based Ship Collision Probability Modeling. Reliability Engineering & System Safety, 96(1), 91–107. doi:10.1016/j.ress.2010.09.003Google Scholar
Gunnar Aarsæther, K. and Moan, T. (2009). Estimating Navigation Patterns from AIS. The Journal of Navigation, 62(4), 587–607. doi:10.1017/s0373463309990129Google Scholar
Habtemariam, B., Tharmarasa, R., McDonald, M. and Kirubarajan, T. (2015). Measurement level AIS/radar fusion. Signal Processing, 106, 348–357. doi:10.1016/j.sigpro.2014.07.029Google Scholar
Han, Z., Jiao, J., Zhang, B., Ye, Q. and Liu, J. (2011). Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR). Pattern Recognition, 44(9), 2170–2183. doi:10.1016/j.patcog.2011.03.002Google Scholar
Harati-Mokhtari, A., Wall, A., Brooks, P. and Wang, J. (2007). Automatic Identification System (AIS): Data Reliability and Human Error Implications. The Journal of Navigation, 60(3), 373–389. doi:10.1017/S0373463307004298Google Scholar
Hartley, R. and Zisserman, A., (2003). Multiple View Geometry in Computer Vision, Second Edition. Cambridge University Press, Cambridge.Google Scholar
Kalal, Z., Mikolajczyk, K. and Matas, J. (2012). Tracking-Learning-Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(7), 1409–1422. doi:10.1109/TPAMI.2011.239Google Scholar
Kanatani, K., Sugaya, Y. and Kanazawa, Y. (2016). Guide to 3D Vision Computation: Geometric Analysis and Implementation. Springer International Publishing. doi:10.1007/978-3-319-48493-8Google Scholar
Maimun, A., Nursyirman, I.F., Sian, A.Y., Samad, R. and Oladokun, S. (2014). Using AIS Data for Navigational Risk Assessment in Restricted Waters. In: Marine Technology and Sustainable Development, Publisher: IGI Global, 245–254. doi:10.4018/978-1-4666-4317-8.ch015Google Scholar
Mou, J.M., Cees, V.D.T. and Ligteringen, H. (2010). Study on Collision Avoidance in Busy Waterways by using AIS data. Ocean Engineering, 37(5), 483–490. doi:10.1016/j.oceaneng.2010.01.012Google Scholar
Safety Department, Maritime Safety Administration, Ministry of Transport of the People's Republic of China. (2018). Analysis on water transport safety situation of year 2017. China Maritime Safety, (1), 14–16. doi:10.16831/j.cnki.issn1673-2278.2018.01.004Google Scholar
Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A. and Shah, M. (2014). Visual tracking: An experimental survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7), 1442–1468. doi:10.1109/TPAMI.2013.230Google Scholar
Somani, D. and Raman, S. (2015). Decision tree for corner detection. IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). doi:10.1109/spices.2015.7091493Google Scholar
Von Gioi, R.G., Jakubowicz, J., Morel, J.M. and Randall, G. (2010). LSD: A Fast Line Segment Detector with a False Detection Control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(4), 722–732. doi:10.1109/tpami.2008.300Google Scholar
Wu, L., Xu, Y., Wang, Q., Wang, F. and Xu, Z. (2016). Mapping Global Shipping Density from AIS Data. The Journal of Navigation, 70(1), 67–81. doi:10.1017/s0373463316000345Google Scholar
Yin, H.P., Chen, B., Chai, Y. and Liu, Z.D. (2016). Vision-based Object Detection and Tracking: A Review. Acta Automatica Sinica, 42(10), 1466–1489. doi:10.16383/j.aas.2016.c150823Google Scholar
Zhang, K. and Song, H. (2013). Real-time visual tracking via online weighted multiple instance learning. Pattern Recognition, 46(1), 397–411. doi:10.1016/j.patcog.2012.07.013Google Scholar
Zhang, W., Goerlandt, F., Kujala, P. and Wang, Y. (2016). An advanced Method for Detecting possible near Miss Ship Collisions from AIS Data. Ocean Engineering, 124, 141–156. doi:10.1016/j.oceaneng.2016.07.059Google Scholar