Final Year Project

Project Title

HU Digital Twin

Description

This project focuses on developing an AI-based system for real-time people detection, tracking, and counting using overhead cameras and deep learning techniques. The aim is to accurately estimate occupancy patterns in indoor environments and integrate this information into a digital twin framework. The system utilizes computer vision models for crowd monitoring and applies tracking methods to handle occlusions and movement across camera views. The purpose of the research is to support smart building management, improve safety monitoring, and enable data-driven decision-making for space utilization and emergency planning. The findings highlight that AI-based vision systems can provide reliable occupancy data, which can enhance digital twin models for better facility management, energy optimization, and efficient crowd management in smart environments.