Image-Based Maritime Obstacle Detection Using Global Sparsity Potentials

In this paper, we present a novel algorithm for image-based maritime obstacle detection using global sparsity potentials (GSPs), in which “global” refers to the entire sea area. The horizon line is detected first to segment the sea area as the region of interest (ROI). Considering the geometric relationship between the camera and the sea surface, variable-size image windows are adopted to sample patches in the ROI. Then, each patch is represented by its texture feature, and its average distance to all the other patches is taken as the value of its GSP. Thereafter, patches with a smaller GSP are clustered as the sea surface, and patches with a higher GSP are taken as the obstacle candidates. Finally, the candidates far from the mean feature of the sea surface are selected and aggregated as the obstacles. Experimental results verify that the proposed approach is highly accurate as compared to other methods, such as the traditional feature space reclustering method and a state-of-the-art saliency detection method.