Marine life airborne observation using HOG and SVM classifier

International audienceThe growth of marine renewable energy and marine protected areas in France leads to a growing need for animal population knowledge at sea. Offshore energy generator projects (wind turbines for example) must obey these regulations and show their harmlessness to the environment, particularly to the wildlife and to protected species, which are vulnerable and threatened. This paper presents a supervised learning method of object detection and classification using numerical HD photography: birds, marine mammals, turtles and sharks (near the surface), ships, other mankind objects (waste). The proposed method is based on HOG (Histogram of Oriented Gradients) features extraction and an SVM (Support Vector Machine) classification process. An evaluation of this method on real data is discussed