Capacity Monitoring Using Object Detection Algorithms

Abstract

Due to the COVID-19/coronavirus pandemic, which started December 2019, in China, many governments around the global have imposed restrict lockdowns across the global along with mask mandates to slow down the spread of the virus. This thesis explores capacity monitoring with the use of the object detection algorithm “You Only Look Once”, more commonly known as YOLO. With the use of real-time CCTV and object detection it would be possible to accurately and quickly determine the capacity of an establishment or of an area, along with detecting whether patrons are wearing masks or even if there are pets there.

The topics of artificial intelligence, machine learning, deep learning, neural networks, artificial neural networks, convolutional neural networks, computer vision, YOLO, raspberry pi, python, TensorFlow and OpenCV are also discussed to help understand how they work and how everything is interconnected.

Finally, the process of setting up a raspberry with camera, installing the OS, installing all of the required libraries, along with YOLO, TensorFlow, and OpenCV are gone through in detail.