ABSTRACT
Remote controlled drone ships without crews on board are expected by the end of the decade. To achieve the goal of developing (semi-)autonomous boats, reliable vision-based methods for vessel detection, classification, and tracking are needed. In this paper, we present a machine learning approach for vessels detection from a moving and zooming camera. In particular, the proposed method is supervised and derives from a fast and robust people detection algorithm. Quantitative experimental results have been obtained on a publicly available data set, which contains images from real sites, demonstrating the effectiveness of the approach. Ground truth annotations and the code of the proposed algorithm are both released for the community.
