With its devastating spread, the ongoing COVID-19 coronavirus pandemic has caused devastation worldwide. Due to the lack of successful restorative medications as well as the shortage of vaccinations against the virus, the communities have been left highly vulnerable. While a handful of countries have vaccinated the majority of their populations, for many countries, the virus still presents a big challenge. Social distancing is considered to be an effective preventative measure against the transmission of the pandemic virus, and virus propagation can be considerably curbed by preventing physical contact between individuals. The objective of this work is, therefore, to provide a depth image-based and cost-effective method for social distance monitoring. A widely used human detection algorithm from depth images has been used to estimate body joint position in real-time. To approximate social distance violations between individuals, the distance between individuals can be estimated, and then, compared to a predefined threshold. Outcomes of the work indicate that the proposed method can successfully identify individuals who violate the social distancing rules, with over 98% detection accuracy. The results are significant, as it can be implemented to assist enforcement agencies to ensure that social distancing rule is abided by the population, to limit the spread of COVID-19 among the population.