Drones equipped with onboard cameras offer promising potential for modern digital media and remote sensing applications. However, effectively tracking moving objects in real time remains a significant challenge. Aerial footage captured by drones often includes complex scenes with dynamic elements such as people, vehicles, and animals. These scenarios may involve large-scale changes in viewing angles, occlusions, and multiple object crossings occurring simultaneously, all of which complicate accurate object detection and tracking. This paper presents an autonomous tracking system that leverages the YOLOv8 algorithm combined with a re-detection mechanism, enabling a quadrotor to effectively detect and track moving objects using only an onboard camera. To regulate the drone’s motion, a PID controller is employed, operating based on the target’s position within the image frame. The proposed system functions independently of external infrastructure such as motion capture systems or GPS. By integrating both positional and appearance-based cues, the system demonstrates high robustness, particularly in challenging environments involving complex scenes and target occlusions. The performance of the optimized controllers was assessed through extensive real-world testing, involving various trajectory scenarios to evaluate the system’s effectiveness. Results confirmed consistent and accurate detection and tracking of moving objects across all test cases. Furthermore, the system exhibited robustness against noise, light reflections, and illumination interference, ensuring stable object tracking even when implemented on low-cost computing platforms.