Deep learning based event recognition in aerial imagery

Name: Deep learning based event recognition in aerial imagery
Author: Şahin, A. H., Ateş, Hasan Fehmi

İsim	Deep learning based event recognition in aerial imagery
Yazar	Şahin, A. H., Ateş, Hasan Fehmi
Basım Tarihi:	2023
Basım Yeri	- IEEE
Konu	Aerial event recognition, Computer vision, Deep learning, Hierarchical dense layers, Wide area imagery
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-835034081-5
Kayıt Numarası	38344417-7936-4ff0-8e2d-388e8ee268a9
Lokasyon	Computer Science
Tarih	2023
Örnek Metin	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

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Deep learning based event recognition in aerial imagery

Yazar Şahin, A. H., Ateş, Hasan Fehmi

Basım Tarihi 2023

Basım Yeri - IEEE

Konu Aerial event recognition, Computer vision, Deep learning, Hierarchical dense layers, Wide area imagery

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-835034081-5

Kayıt Numarası 38344417-7936-4ff0-8e2d-388e8ee268a9

Lokasyon Computer Science

Tarih 2023

Örnek Metin 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