Maritime situation awareness is the core of ship navigation and determines the development direction of smart ships. Visual perception becomes an active yet important research task in maritime traffic. To aim that, this paper introduces three situation awareness methods based on visual images. The first method uses kernelized correlation filtering algorithms and Kalman filter models to solve ship occlusion in the image and achieve accurate ship tracking tasks; The second method uses kernelized correlation filtering and logarithmic polar coordinate transformation to solve the imaging size changes during ship navigation; The third method uses yolo algorithms to obtain the high-quality distance information from port videos. The experimental results and quantitative indicators show that each method can effectively achieve reliable maritime situational awareness tasks. The comprehensive realization of maritime situation awareness can better reduce risks and ensure the safety of ships.


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    Title :

    An overview of robust maritime situation awareness methods


    Contributors:
    Xu, Xueqian (author) / Chen, Xinqiang (author) / Wu, Bing (author) / Yip, Tsz Leung (author)


    Publication date :

    2021-10-22


    Size :

    1111753 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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