Auth-Persons: A Dataset for Detecting Humans in Crowds from Aerial Views

Recent advances in artificial intelligence, control and sensing technologies have facilitated the development of autonomous Unmanned Aerial Vehicles (UAVs). Detecting humans from video input captured on-the-fly from UAVs is a critical task for ensuring flight safety, mostly handled with lightweight Deep Neural Networks (DNNs). However the detection of individual people in the case of dense crowds and/or distribution shifts (i.e., significant visual differences between the training and the test sets) is still very challenging. This paper presents AUTH-Persons, a new, annotated, publicly available video dataset, that consists of both real and synthetic footage, suitable for training and evaluating aerial-view person detection algorithms. The synthetic data were collected from 8 visually distinct photorealistic outdoor environments and they mostly contain scenes with crowded areas, where heavy occlusions and high person densities pose challenges to common detectors. This dataset is employed to evaluate the generalization performance of various state-of-the-art detection frameworks, by testing them on environments that are visually distinct from those they have been trained on. Finally, given that Non-Maximum Suppression (NMS) methods at the end of person detection pipelines typically suffer in crowded scenes, the performance of various NMS algorithms is also compared in AUTH-Persons.