K-Zones: a Machine Learning-Based System to Estimate Social Distancing Violations During Pandemic Eras

Abstract

The outbreak of pandemics adversely influences various aspects of people's lives, including economies, education, careers, and social relations. Therefore, many authorities worldwide resort to imposing social distancing regulations to flatten the curve of new confirmed cases. This paper proposes a Machine Learning-based social distancing violation detection system. Unlike many contributions in the literature that use pairwise distance computation running in quadratic execution time, this paper introduces a novel technique that runs in linear time. The solution is considered a Video Surveillance System, and the experimental results show how the system effectively detects not only social distancing violations but also the severity of those violations.