A Fast Object Detector Based on High-Order Gradients and Gaussian Process Regression for UAV Images

Unmanned aerial vehicles (UAVs) acquire images characterized by an exceptional level of detail, which calls for processing and analysis methods capable of efficiently exploiting their rich information content. In particular, the detection of specific classes of objects (e.g. cars, roofs) represents an important but challenging task for these images. Most of the related literature has aimed at proposing methods capable of providing satisfactory detection accuracies. However, they typically refer to a specific class of objects and give little attention to the processing time. In this work, we present a novel and fast methodological alternative. In addition to being particularly fast, the proposed method is a general detection approach that can be customized to any class of objects after an opportune training phase. It consists of the design of a non-linear filter that combines image gradient features at different orders and Gaussian process (GP) modelling. High-order image gradients perm...