We successfully demonstrated a proof of concept for an autonomous object tracking system using a quadcopter equipped with stereo cameras, AprilTags, and a motion capture system. The system was able to detect and track the position of an AprilTag located on the ground, sending data via MAVLink to the BeagleBone Blue, which controlled the quadcopter’s flight. Despite challenges such as a slight delay in the system’s response time, some limitation in the range and accuracy of the AprilTag detection, and unexpected movement of the quadcopter in a confined space, we were able to demonstrate the core functionality of the individual components, such as the Raspberry Pi 5 for vision processing, the BeagleBone Blue for control, and the OptiTrack system for state estimation. While the quadcopter successfully tracked the AprilTag for brief periods during the flight, the overall tracking performance was inconsistent. Factors such as the limited detection range for the cameras, the extra weight from additional components, and the need for better altitude control contributed to some instability. Nonetheless, this project highlights the potential of integrating low-cost hardware and open-source software for autonomous tracking tasks. To improve the overall performance of the system in future work, several key enhancements could be made. First, increasing the size of the AprilTag markers would significantly improve the detection range and reliability, particularly at higher altitudes or greater distances. A larger tag would help mitigate the issue of losing visibility during flight, especially when the quadcopter moves away from the target. Another area for improvement is the reduction of the quadcopter’s overall weight. By utilizing lighter materials or optimizing the design of components such as the legs, propeller guards, and camera mounts, the quadcopter would experience improved flight stability and longer battery life. With reduced power consumption, the quadcopter would be better able to maintain hover capabilities and improve tracking accuracy. Additionally, integrating motion capture with the AprilTag system would enhance the real-time positional feedback, allowing for more precise alignment between the setpoint and the quadcopter’s actual position. This would enable more accurate control by providing continuous, dynamic adjustments to the quadcopter’s position in relation to the AprilTag. These proposed improvements would address challenges related to system robustness and stability. They would make the system more reliable and better suited for real-world applications that require accurate object tracking. By refining control algorithms, enhancing sensor accuracy, and optimizing the overall hardware design, we can develop a more capable and dependable autonomous tracking system.