MODERN COMPUTER VISION AND DEEP LEARNING APPROACHES FOR FACE MASK AND SOCIAL DISTANCING DETECTION
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Abstract
This thesis proposes a new automated pipeline to detect and localize ob-
jects in a given video scene using state-of-the-art computer vision models and machine
learning. The inspiration of this pipeline was the ongoing global pandemic of COVID-
19, which the World Health Organization (WHO) declared as a global pandemic 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 imple-
mented 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.