Automated Runway Occupancy Time Computation Using Multi-Camera Computer Vision Tracking

This project will train the intern in using artificial intelligence (AI) and computer vision to improve aircraft tracking in airports. Specifically, the intern will work on automating the measurement of the Runway Occupancy Time (ROT) in aircraft, reducing the need for manual tracking and increasing accuracy. The project involves designing a multi-camera system to track aircraft from different angles, developing AI models for precise aircraft classification, and implementing an automated system to measure ROT efficiently. By the end of the project, the intern will gain hands-on experience in AI integration and system performance evaluation. These advancements will help improve airport safety, streamline operations, and create scalable solutions adaptable to different airport environments.

Faculty Supervisor:

Chul Min Yeum

Student:

Partner:

NAV Canada

Discipline:

Engineering

Sector:

Transportation and warehousing

University:

University of Waterloo

Program: