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    Modelling land water composition scene for maritime traffic surveillance

    Pang, Shaoning; Zhao, Jing (Jane); Hartill, B.; Sarrafzadeh, Hossein

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    Date
    2016
    Citation:
    Pang, S., Zhao, J., Hartill, B., & Sarrafzadeh, A. (2016). Modelling land water composition scene for maritime traffic surveillance. International Journal of Applied Pattern Recognition, 3(4), pp.324-350.
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/3802
    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, marine traffic, land and water composition scene, dynamic learning rate
    ANZSRC Field of Research:
    080110 Simulation and Modelling
    Copyright Holder:
    Inderscience Enterprises
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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