Şahin, A. H.Ateş, Hasan Fehmi2024-02-162024-02-162023979-835034081-5http://hdl.handle.net/10679/9156https://doi.org/10.1109/UBMK59864.2023.10286774In this paper, we investigate event recognition for aerial surveillance. This is a significant task especially when we consider the growing popularity of UAVs. The main purpose of the paper is to detect events both at the clip level in aerial videos and also at the frame level in aerial images. To achieve this goal, novel deep learning models and training techniques are used. In this work, we propose new model architectures to detect events in both image and video domains. The developed models are tested on the ERA dataset. Results show that the proposed models achieve state-of-the-art performance on both single images and aerial video clips of the ERA dataset.engrestrictedAccessconferenceObject42643110.1109/UBMK59864.2023.10286774Aerial event recognitionComputer visionDeep learningHierarchical dense layersWide area imagery2-s2.0-85177554022