  The practice of social distancing is imperative to curbing the spread of
contagious diseases and has been globally adopted as a non-pharmaceutical
prevention measure during the COVID-19 pandemic. This work proposes a novel
framework named SD-Measure for detecting social distancing from video footages.
The proposed framework leverages the Mask R-CNN deep neural network to detect
people in a video frame. To consistently identify whether social distancing is
practiced during the interaction between people, a centroid tracking algorithm
is utilised to track the subjects over the course of the footage. With the aid
of authentic algorithms for approximating the distance of people from the
camera and between themselves, we determine whether the social distancing
guidelines are being adhered to. The framework attained a high accuracy value
in conjunction with a low false alarm rate when tested on Custom Video Footage
Dataset (CVFD) and Custom Personal Images Dataset (CPID), where it manifested
its effectiveness in determining whether social distancing guidelines were
practiced.
