Author
Şahin, A. H., Ateş, Hasan Fehmi
Publication Date
2023
Publication Place
-
IEEE
Subject
Aerial event recognition, Computer vision, Deep learning, Hierarchical dense layers, Wide area imagery
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
979-835034081-5
Record ID
38344417-7936-4ff0-8e2d-388e8ee268a9
Library Location
Computer Science
Date
2023
Sample Text
In 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.
DOI
10.1109/UBMK59864.2023.10286774
