Social Distancing Detector Using Yolo Network
S Sanjana, N M Shruthi, Chaitanya kumar B, R Thejas, Nalajam Geethanjali · Journal of Emerging Technologies and Innovative Research · 2021
The Coronavirus Disease, commonly known as COVID-19, has swept the world by storm. The most serious concern is that it spreads from person to person if they are in proximity. As a result, precautions such as following Social Distancing rules and wearing masks are necessary. However, many people are finding it difficult to adhere to social distancing standards due to personal obligations. As a result, Flattening the coronavirus case curve is not an easy task, and it is critical to recognize people in real-time and determine whether they are adhering to social distancing rules. Our study focuses on detecting people using YOLO object tracking model which is well-known for its speed and accuracy. Taking the center points of the bounding boxes produced by the model, we apply Euclidean distance to find the distance between two people's bounding boxes. If the distance between two points is small, the bounding box containing those points turns red, and it can be used to warn individuals not to break the rules and to take appropriate action.