Computer Vision and Ship Traffic Analysis: Inferring Maneuver Patterns From the Automatic Identification System

• Autorzy: Moan T. , Aarsæther K.
• Języki publikacji: EN

Abstrakty

EN

The Automatic Identification System has proven itself as a valuable source for ship traffic information. Its introduction has reversed the previous situation with scarcity of precise data from ship traffic and has instead posed the reverse challenge of coping with an overabundance of data. The number of time series available for ship manoeuvring analysis has increased from tens, or hundreds, to several thousands. Sifting through this data manually, either to find the salient features of traffic, or to provide statistical distribu-tions of decision variables is an extremely time consuming procedure. In this paper we present the results of applying computer vision techniques to this problem and show how it is possible to automatically separate AIS data in order to obtain traffic statistics and prevailing features down to the scale of individual manoeuvres and how this procedure enables the production of a simplified model of ship traffic.

Słowa kluczowe

EN

Ship Traffic Analysis; computer vision; Automatic Identification System (AIS); Inferring Maneuver Patterns; manoeuvering; Marine Traffic Control; MATLAB; Navigational Aids

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2010
• Tom: Vol. 4, no. 3
• Strony: 303--308
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Moan T.

• Norwegian University of Science and Technology, Trondheim, Norway

autor

Aarsæther K.

• Norwegian University of Science and Technology, Trondheim, Norway

Bibliografia

• 1. Aarsæther K. G. & Moan T. 2007. Combined Maneuvering Simulations, AIS and full-mission simulations. In Adam Weintrit (ed) Advances in marine navigation and safety of sea transportation; proc 7. intern. symp. on navigation Gdynia 20-22 June 2007
• 2. Azzalini A. 1985. A class of distributions which include the normal ones. Scandinavian Journal of Statistics 12:171-178
• 3. Brown L. G. 1992. A survey of image registration techniques. ACM computing Surveys 24:325-376
• 4. Gucma L. & Goryczko E. 2007. The implementation of oil spill cost model in the southern Baltic Sea area to assess the possible losses due to ship collisions. In Adam Weintrit (ed) Advances in marine navigation and safety of sea trans-portation; Proc 7. intern. symp. on navigation Gdynia 20-22 June 2007
• 5. Hutchison, B.L.; Gray, D.L. & Mathai, T., 2003. Maneuvering simulations - an application to waterway navigability. Transactions of the Society of Naval Architects and Marine Engineers Vol 111:485-516
• 6. Merrick J.R.W.; van Dorp J.R.; Blackford J.P.; Shaw G.L.; Harrald J. & Mazzuchi T.A. 2003. A traffic density analysis of proposed ferry service expansion in San Francisco Bay using a maritime simulation model. Reliability Engineering and System Safety. Vol 81(2):119-132
• 7. Vetterling W.T.; Press H. P.; Teukolsky S. A.; Flannery B. P. 2007. Numerical recipes – the art of scientific computing. Cambridge university press
• 8. Zitová B. Flusser J. 2003. Image registration methods: a sur-vey. Image and vision computing 21(11):977-1000