A master’s thesis at the Northern Technical University discusses the design and implementation of an intelligent system that helps in detecting and tracking people

I researched a technical master’s thesis in the Engineering Technical College / Mosul, specializing in computer technology engineering, tagged:
Real-Time Human Detection and Tracking Based on Deep Learning Techniques for Social Distance
The thesis showed: – Presented by the researcher (Gouna Muhammad Zahir) the design and implementation of an intelligent system to detect and track people for social distancing using deep learning algorithms in real-time with the help of a Raspberry Pi and a webcam. The system was implemented using a database that was prepared in advance to build a YOLOv5s model to devise a new model It is M-YOLOv5s for network training.
The letter aimed to:

Knowing the possibility of adopting the YOLOv5 model to identify and track people in real-time
Monitoring the state of social distancing in real-time using the bird’s eye view algorithm that is based on the application of the Euclidian formula
Obtaining a higher accuracy in discrimination, as the system performance accuracy reached 100%.
The thesis recommended: – to improve the performance of the proposed system through the:
Install several cameras to detect people from several directions and angles while collecting a larger number of photos and videos to obtain a larger database in size and improve the process of discovery and identification of people, as well as using other methods of deep learning and making a comparison with them to determine the best method
Part of the discussion was attended by the Dean of the College, Prof. Dr. Majid Khalil Najm, the scientific and administrative assistant, the head of the Computer Technology Engineering Department, and several professors and postgraduate students.
The discussion committee consisted of: