Abstract
In the case of the fight against COVID-19, social distancing has proven to be an effective intervention to minimize disease transmission. AI/Deep Learning has recently lent itself as a great tool to solving almost every conceivable problem in daily life and has shown significant promising results for such problems. In this article, we will explore in-depth how Python, Computer Vision, and Deep Learning can be used to track social distances in public and workplaces. Our study seeks to contribute to the effort in the fight against the pandemic by providing a tool that basically functions as a social distancing monitoring device. Since social distancing is one of the first-line, non-pharmaceutical interventions adopted to combat the COVID-19 pandemic and with growing evidence supporting its effectiveness, we believe that strict accordance with this policy should be encouraged. With the sole purpose of ensuring effective social distancing in public places, our tool will analyze real-time and/or pre-recorded video feed, detect and compute the pairwise metric distances between individuals to validate whether or not social distancing is maintained.
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Class-Peters, F. et al. (2022). Post-COVID-19: Deep Image Processing AI to Analyze Social Distancing in a Human Community. In: Saeed, F., Al-Hadhrami, T., Mohammed, E., Al-Sarem, M. (eds) Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing, vol 1399. Springer, Singapore. https://doi.org/10.1007/978-981-16-5559-3_6
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