Continuous Social Distance Monitoring in Indoor Space

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Abstract

The COVID-19 pandemic has caused over 4 million deaths since 2020. To contain the spread of the virus, social distancing is one of the most simple yet effective approaches. Motivated by this, in this paper we study the problem of continuous social distance monitoring (SDM) in indoor space, in which we can monitor and predict the pairwise distances between the users in a building in real time. These can also serve as the fundamental service for downstream applications, e.g., a mobile alert application that prevents users from potential close contact with others. To facilitate the monitoring process, we propose a framework that takes the current and future uncertain location of the objects into account, and finds the object pairs that are close to each other in a near future. We develop efficient algorithms to update the result when object locations update. Extensive experiments on both real and synthetic datasets were carried out that verified the efficiency and effectiveness of our proposed framework and algorithms.