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.
International Journal of Applied Pattern Recognition, 2016 Vol.3 No.4, pp.324 - 350
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