College - Author 1

College of Engineering

Department - Author 1

Computer Engineering Department

Degree Name - Author 1

BS in Computer Engineering

Date

6-2020

Primary Advisor

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

Abstract/Summary

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

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