College - Author 1

College of Engineering

Department - Author 1

Computer Engineering Department

Degree Name - Author 1

BS in Computer Engineering



Primary Advisor

Joseph Callenes-Sloan, College of Engineering, Electrical Engineering Department


The growth of drone technology goes with the demands of computer vision. Computer vision provides opportunities for developers to design and create, giving rise to a new innovation. It is also a basis for a full autonomous system when implemented with an intelligent agent. Surveillance drones are installed with cameras, and cameras are the most common approach to computer vision. However, a high-resolution camera with great performance could be expensive, while other low-cost cameras might lack reading accuracy. To improve the performances and maximize a camera’s capability, many different object trackers come into play. This paper will investigate and study three different object trackers in video streams with a goal to determine an effective object tracker for an indoor surveillance drone to archive a robust drone landing and localization.

Available for download on Wednesday, June 11, 2025