COVID-19 Social Distance Surveillance Using Deep Learning

COVID-19 disease discovered from the novel corona virus can spread through close contact with a COVID-19 infected person. One of the measures advised to contain the spread of the virus is to maintain social distancing by minimizing contact between potentially infected individuals and healthy individuals or between population groups with high rates of transmission and population groups with no or low-levels of transmission. Motivated by this practice, we propose a deep learning framework for social distance detection and monitoring using surveillance video that can aid in reducing the impact of COVID-19 pandemic. This work utilizes YOLO, Detectron2 and DETR pre-trained models for detecting humans in a video frame to obtain bounding boxes and their coordinates. Bottom-centre points of the boxes were determined and were then transformed to top-down view for accurate measurement of distances between the detected humans. Based on the depth of each bottom-centre point estimated using monodepth2, dynamic distance between pairs of bounding boxes and corresponding distance threshold (safe distance) to prevent violation of social distancing norm were computed. Bounding boxes which violate the distance threshold were categorized as unsafe. All the experiments were conducted on publicly available Oxford Town Center, PETS2009 and VIRAT dataset. Results showed that Detectron2 with top-down view transformation and distance thresholding using pixel depth estimation outperformed other state-of-the-art models. The major contribution of this work is the estimation and integration of variable depth information in obtaining the distance threshold for evaluating social distances between humans in videos. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Blockchain and FOT (Fog of Things) based Framework and Technique for Anticipating an Infectious Illness Sent by a Harmful Respiratory Infection

The current exploration is by and large identified with a framework and technique for anticipating an irresistible sickness, for example, COVID-19 communicated by a harmful respiratory infection. Revealed are a framework and technique for anticipating an irresistible infection communicated by a harmful respiratory infectionThe system joins a larger part of Internet of Things (IoT) sensors, a larger part of fog center devices, and a greater part of handling contraptions in cloud server ranches. The IoT sensors are organized to be joined to a larger part of individuals to deliver a prosperity dataset. The Fog registering devices are connected with a haze lay-er to get the prosperity dataset from the IoT sensors to quantify and store the prosperity dataset over a square chain organization. The contraptions measure the prosperity da-taset at the mist layer by playing out haze handling. The figuring contraptions and cloud server ranches get the taken care of prosperity dataset from the haze center point devices over the blockchain network. This assessment is in like manner requested of for the patent in Indian Patent Office with application number - 202011021969. © 2021 IEEE.

Mapping Spread Virus COVID-19 in India Using K-Means Clustering

Covid have confirmed as pandemic global by the World Health Organization (WHO), because spread that very fast among humans. As a result of the Covid-19 virus, many infected patients died, including from all countries on the Asian continent. Like the case that occurred in one of the Asian countries, namely India, which is one of the countries that experienced a spike in Covid-19 cases, the transmission of thevirus Covid-19 in India penetrated more than 400,000 cases in 1 day. The number is the highest daily record set by India during the Covid-19 pandemic. However, it was found that the problem of the spread of Covid-19 tends to increase, this is the country with the second largest population in the world. The total number of Covid-19 cases in the country has reached 21 million or second only to the United States. The vastness of India's territory allows the need for grouping the parts by region in India. This grouping produces the center points for the spread of Covid-19 cases. The purpose of grouping Covid-19 cases based on clusters is to find out the weight/percentage value generated from each of these clusters using the K-Means Clustering method. This method is used to map the spread of the Covid-19 virus from various regions in India based on confirmed cases, dead, recovered and active/new clusters. The benefits obtained for the government in overcoming Covid-19 cases are to create strategies to prevent the spread of Covid-19 based on information from the results of regional clustering in India The results obtained from research conducted in 38 regions in India using 4 clusters resulted in Confirmed cases (C0) 199 items, Died (C1) 779 items, Recovered (C2) 21 items, and Active/new cluster (C3) 231 items with a totalcluster of 1230items. © 2021 IEEE.