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In recent years, with the development of the marine industry, navigationenvironment becomes more complicated. Some artificial intelligencetechnologies, such as computer vision, can recognize, track and count thesailing ships to ensure the maritime security and facilitates the managementfor Smart Ocean System. Aiming at the scaling problem and boundary effectproblem of traditional correlation filtering methods, we propose aself-selective correlation filtering method based on box regression (BRCF). Theproposed method mainly include: 1) A self-selective model with negative samplesmining method which effectively reduces the boundary effect in strengtheningthe classification ability of classifier at the same time; 2) A bounding boxregression method combined with a key points matching method for the scaleprediction, leading to a fast and efficient calculation. The experimentalresults show that the proposed method can effectively deal with the problem ofship size changes and background interference. The success rates and precisionswere higher than Discriminative Scale Space Tracking (DSST) by over 8percentage points on the marine traffic dataset of our laboratory. In terms ofprocessing speed, the proposed method is higher than DSST by nearly 22 FramesPer Second (FPS).

Self-Selective Correlation Ship Tracking Method for Smart Ocean System