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.

Date Published: 19 March 2013
PDF: 8 pages
Proc. SPIE 8663, Video Surveillance and Transportation Imaging Applications, 86630D (19 March 2013); doi: 10.1117/12.2000452

Published in SPIE Proceedings Vol. 8663:
Video Surveillance and Transportation Imaging Applications
Robert Paul Loce; Eli Saber, Editor(s)