Can I read Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies on EtoBox?

Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies by Imran Ahmed; Gwanggil Jeon; Abdellah Chehri; Mohammad Mehedi Hassan is a Environmental Science article available to read on EtoBox.

What is Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies about?

Nowadays, deep neural networks are widely applied in sustainable smart cities and societies, including smart manufacturing, healthcare, industries, agriculture, surveillance, and various artificial intelligence-based real-life applications. In this regard, the human detection system has gained notable attention since it is recognized as a crucial task in intelligent surveillance applications. Researchers practiced a variety of computer vision and deep neural networks-based techniques for human detection-based applications; however, they often focused on the frontal view camera perspective. Thus, in this work, we have introduced a human detection system for intelligent surveillance in smart cities and societies with a completely distinct perspective, i.e., an overhead perspective that can provide sufficient visibility and coverage of a scene in congested and obstructed environments. However, human appearance can be difficult from such an extreme point of view, as there are significant variations in humans' poses and appearances. Therefore, in this work, leveraging the deep neural network-based object detection technique, the Gaussian YOLOv3 algorithm is used for human detection. The

Who reads Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies?

Author

Imran Ahmed; Gwanggil Jeon; Abdellah Chehri; Mohammad Mehedi Hassan

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)