35th Annual Student Research Conference
Utilizing Computer Vision to Enforce COVID Precautionary Measures
Mohammadhossein Momeni
Dr. Kafi Rahman, Faculty Mentor
Tracking social distancing, contact tracing, and many other COVID regulations have become essential with the emergence of the COVID pandemic. In this work, we incorporate computer vision techniques in order to measure the distance between two individuals, verify whether each person is wearing a mask, and provide feedback to ensure individuals are maintaining social distancing in a classroom setting. The process analyzes the live pictures at regular intervals and uses them for contact tracing purposes. In academia and professional settings, the proposed work can be useful to enforce government mandated social distancing and other preventative measures in an automated fashion. In this work, we develop a proof of concept prototype of the proposed system and present our findings.
Keywords: COVID, Computer Vision, Machine Learning
Topic(s):Computer Science
Presentation Type: Oral Presentation
Session: 107-3
Location: MG 1098
Time: 9:00