Peddinti Bharati, Shaikh Amir, K R Bhavya, K C Nithin Kumar
Department of Computer Science, Graphic Era Deemed to be University, Dehradun, India.
Department of Mechanical Engineering, Graphic Era Deemed to be University, Dehradun, India.
Biomed Signal Process Control. 2021 Jul;68:102605. doi: 10.1016/j.bspc.2021.102605. Epub 2021 Apr 1.
The novel Corona Virus (COVID-19) has become the reason for the world to declare it as a global pandemic, which has already taken many lives from all around the world. This pandemic has become a disaster since the spreading rate from person to person is incredibly high and many techniques have come forth to aid in stopping the infection. Although various types of methods have been put into implementation, the search and suggestions of new approaches to reduce the increasing rate of infection will never come to an end until a vaccine terminates this pandemic. This study focuses on proposing a new framework that is based on Deep Learning algorithms for recognizing the COVID-19 cases, mostly in public places. The algorithms include Background Subtraction for extracting the foreground of thermal images from thermal videos generated by Thermal Cameras through the Thermal Imaging process and the Convolutional Neural Network for detecting people infected with the virus. This automated prototype works in a real-time scenario that helps identify people with the disease and will try to trace it while separating them from having any other contact. This proposal intends to achieve a satisfying growth in determining the real cases of COVID-19 and minimize the spreading rate of this virus to the max, ultimately avoiding more deaths.
新型冠状病毒（COVID-19）已成为世界宣布其为全球大流行病的原因，这场大流行病已在世界各地夺走了许多人的生命。由于人际传播率极高，这场大流行病已演变成一场灾难，并且已经出现了许多技术来帮助阻止感染。尽管已经实施了各种方法，但在疫苗终结这场大流行病之前，寻找和提出降低不断上升的感染率的新方法的工作永远不会停止。本研究着重提出一个基于深度学习算法的新框架，用于识别COVID-19病例，主要是在公共场所。这些算法包括用于通过热成像过程从热成像相机生成的热视频中提取热图像前景的背景减法，以及用于检测感染病毒的人的卷积神经网络。这个自动化原型在实时场景中运行，有助于识别患病人员，并在将他们与其他人隔离开来的同时尝试追踪病毒。本提议旨在在确定COVID-19实际病例方面取得令人满意的进展，并最大限度地降低这种病毒的传播率，最终避免更多死亡。
