Int. J. of Applied Pattern Recognition   »   2016 Vol.3, No.4

 

 

Title: Modelling land water composition scene for maritime traffic surveillance

 

Authors: Shaoning Pang; Jing Zhao; Bruce Hartill; Abdolhossein Sarrafzadeh

 

Addresses:
Department of Computing, Unitec Institute of Technology, Private Bag 92006, Auckland 1025, New Zealand
Department of Computing, Unitec Institute of Technology, Private Bag 92006, Auckland 1025, New Zealand
National Institute of Water and Atmospheric Research, 41 Market Place, Viaduct Harbour, Auckland 1010, New Zealand
Department of Computing, Unitec Institute of Technology, Private Bag 92006, Auckland 1025, New Zealand

 

Abstract: Background modelling, used in many vision systems, must be robust to environmental change, yet sensitive enough to identify all moving objects of interest. Existing background modelling approaches have been developed to interpret images in terrestrial situations, such as car parks and stretches of road, where objects move in a smooth manner and the background is relatively consistent. In the context of maritime boat ramps surveillance, this paper proposes a cognitive background modelling method for land and water composition scenes (CBM-lw) to interpret the traffic of boats passing across boat ramps. We compute an adaptive learning rate to account for changes on land and water composition scenes, in which a geometrical model is integrated with pixel classification to determine the portion of water changes caused by tidal dynamics and other environmental influences. Experimental comparative tests and quantitative performance evaluations of real-world boat-flow monitoring traffic sequences demonstrate the benefits of the proposed algorithm.

 

Keywords: background modelling; moving object detection; moving objects; maritime traffic surveillance; maritime surveillance; land and water composition scenes; dynamic learning rate; boat ramps; boat traffic; boats; boat surveillance; geometric models; water changes; traffic monitoring; traffic sequences.

 

DOI: 10.1504/IJAPR.2016.10002986

 

Int. J. of Applied Pattern Recognition, 2016 Vol.3, No.4, pp.324 - 350

 

Submission date: 30 Aug 2016
Date of acceptance: 01 Sep 2016
Available online: 09 Feb 2017

 

 

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