Artificial intelligence framework for threat assessment and containment for covid-19 and future epidemics while mitigating the socioeconomic impact to women, children, and underprivileged groups

With the emergency situation that arises with COVID-19, the intense containment strategies adopted by many countries had little or no consideration towards socio-economic ramifications or the impact on women, children, socio­economically underprivileged groups. The existence of many adverse impacts raises questions on the approaches taken and demands proper analysis, scrutiny and review of the policies. Therefore, a framework was developed using the artificial intelligence (Al) techniques to detect, model, and predict the behaviour of the COVID-19 pandemic containment strategies, understanding the socio-economic impact of these strategies on identified diverse vulnerable groups, and the development of AI-based solutions, to predict and manage a future spread of COVID or similar infectious disease outbreaks while mitigating the social and economic toil. Based on generated behaviour and movements, Al tools were developed to conduct contact tracing and socio-economic impact mitigation actions in a more informed, socially conscious and responsible manner in the case of the next wave of COVID-19 infections or a different future infectious disease. © 2022, National Science Foundation. All rights reserved.

Hands Off: A Handshake Interaction Detection and Localization Model for COVID-19 Threat Control

The COVID-19 outbreak has affected millions of people across the globe and is continuing to spread at a drastic scale. Out of the numerous steps taken to control the spread of the virus, social distancing has been a crucial and effective practice. However, recent reports of social distancing violations suggest the need for non-intrusive detection techniques to ensure safety in public spaces. In this paper, a real-time detection model is proposed to identify handshake interactions in a range of realistic scenarios with multiple people in the scene and also detect multiple interactions in a single frame. The efficacy of the proposed model was evaluated across two different datasets on more than 3200 frames, thus enabling a robust localization model in different environments. The proposed model is the first dyadic interaction localizer in a multi-person setting, which enables it to be used in public spaces to identify handshake interactions and thereby identify and mitigate COVID-19 transmission. © 2021 IEEE.

Integrated Video Based Crowdedness Forecasting Framework with a Review of Crowd Counting Models

Crowd counting and forecasting is an important problem amidst Covid 19 circumstances. A unified system to automate crowd monitoring, collect data about crowdedness and predict future crowds is presented in this paper. An evaluation of existing state-of-the-art crowd counting algorithms on a novel dataset is conducted in the first part of the paper, which demonstrates the shortcomings of these algorithms. Several novel algorithms, including a densely connected neural network, convolutional neural network, and a long short term memory based recurrent neural network, for predicting crowd counts in the near and distant future are presented afterwards in the second half of the paper. © 2021 IEEE.