Robust moving ship detection using context-based motion analysis and occlusion handling

This paper proposes an original moving ship detection approach in video surveillance systems, especially con- centrating on occlusion problems among ships and vegetation using context information. Firstly, an over- segmentation is performed to divide and classify by SVM (Support Vector Machine) segments into water or non-water, while exploiting the context that ships move only in water. We assume that the ship motion to be characterized by motion saliency and consistency, such that each ship distinguish itself. Therefore, based on the water context model, non-water segments are merged into regions with motion similarity. Then, moving ships are detected by measuring the motion saliency of those regions. Experiments on real-life surveillance videos prove the accuracy and robustness of the proposed approach. We especially pay attention to testing in the cases of severe occlusions between ships and between ship and vegetation. The proposed algorithm outperforms, in terms of precision and recall, our earlier work and a proposal using SVM-based ship detection.