Social distance capturing and alerting tool

Yeshasvi, M.; Bind, V.; Subetha, T.

The Novel Coronavirus outbreak worldwide has made people think about the possibility of protecting themselves from infections. Various precaution measures like social distancing, maintain hand hygiene, stay away from crowded places, refrain touching eyes, mouth, and nose. The pandemic has also challenged various researchers, mathematicians, Pharmacists etc., to dig out the solutions in this pand emic situation. Machine learning concepts and algorithms also find a great place amidst various scientists. Among all the preventive measures, social distancing plays a vital role in flattening the COVID-19 curve. This paper proposes an effective social distancing capturing and alerting tool to detect humans if they are not maintaining the social distance. The system acquires images/videos as input and process it to detect the Region of Interest (ROI) and human in the image frame. Then, the pairwise distance is computed between all the identified people and depending on the distance value obtained the system will alert the people who are not maintaining the social distance. The system is evaluated for various image input acquisition techniques like image, video, and camera image/video to compute the performance of the system. Experimental results shows that the proposed system obtains a better precision of 99.7% for images and 97% recall for videos. The system can also be extended to applications like human tracking, pedestrian detection, and vehicle tracking. © 2021 IEEE.