Modern computer vision and deep learning approaches for face mask and social distancing detection

Abstract

This thesis proposes a new automated pipeline to detect and localize objects in a given video scene using state-of-the-art computer vision models and machine learning. This pipeline was inspired by the ongoing global pandemic of COVID-19, which was declared a global pandemic by the World Health Organization (WHO) on January 30, 2020. The objective for this pipeline was to employ modern technologies, such as machine learning and deep learning, to help solve a real-life problem: face-mask wearing and social distancing in indoor and outdoor places. Employing state-of-the-art object detection algorithms along with a deep learning-based approach to estimate distance between people, we have created a pipeline to receive a CCTV live camera feed and output heat-maps showing whether people are adhering to face mask-wearing and social distancing. Training over 39K images on the-sate-of-art object detection models at the time, YOLOv4 and Faster R-CNN with Focal Loss, we have achieved over 83\% Mean Average Precision (mAP) on a custom dataset. In addition, we implemented classical and modern approaches to measure distances between people in public spaces, such a projective transformation and deep learning-based models, to estimate camera calibration parameters. ☐ Keywords: Aerial Surveillance, Object Detection, Object Tracking, Dataset, Benchmark, YOLO, Faster R-CNN