19 March 2013 Ship detection in port surveillance based on context and motion saliency analysis
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This paper presents an automatic ship detection approach for video-based port surveillance systems. Our approach combines context and motion saliency analysis. The context is represented by the assumption that ships only travel inside a water region. We perform motion saliency analysis since we expect ships to move with higher speed compared to the water flow and static environment. A robust water detection is first employed to extract the water region as contextual information in the video frame, which is achieved by graph-based segmentation and region-based classification. After the water detection, the segments labeled as non-water are merged to form the regions containing candidate ships, based on the spatial adjacency. Finally, ships are detected by checking motion saliency for each candidate ship according to a set of criteria. Experiments are carried out with real-life surveillance videos, where the obtained results prove the accuracy and robustness of the proposed ship detection approach. The proposed algorithm outperforms a state-of-the-art algorithm when applied to the same sets of surveillance videos.
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Xinfeng Bao, Xinfeng Bao, Svitlana Zinger, Svitlana Zinger, Rob Wijnhoven, Rob Wijnhoven, Peter H. N. de With, Peter H. N. de With, "Ship detection in port surveillance based on context and motion saliency analysis", Proc. SPIE 8663, Video Surveillance and Transportation Imaging Applications, 86630D (19 March 2013); doi: 10.1117/12.2000452; https://doi.org/10.1117/12.2000452


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