A novel target detection algorithm combining foreground and background manifold-based models

This paper focuses on the detection of small objects—more precisely on vehicles in aerial images—on complex backgrounds such as natural backgrounds. A key contribution of the paper is to show that, in such situations, learning a target model and a background model separately is better than training a unique discriminative model. This contrasts with standard object detection approaches for which objects vs. background classifiers use the same model as well as the same types of visual features for both. The second contribution lies in the manifold learning approach introduced to build these models. The proposed detection algorithm is validated on the publicly available OIRDS dataset, on which we obtain state-of-the-art results.