Real-Time UAV Tracking: A Comparative Study of YOLOv8 with Object Tracking Algorithms

Abstract

The use of unmanned aerial vehicles (UAVs) has increased rapidly, leading to an effort to accurately and efficiently track UAVs. Many existing approaches utilize YOLO, a state-of-the-art object detection model, in conjunction with object tracking algorithms to detect and follow UAVs in real-time. However, these systems typically focus on a single method, without considering alternative tracking methods. In this paper, we present an experimental comparison of multiple object tracking algorithms integrated with YOLOv8, offering a comprehensive evaluation of their performance in UAV tracking scenarios. First, the model size was optimized to determine the best balance between speed and accuracy. Then, various tracking methods are tested to determine the most effective combination. The YOLOv8 model combined with a Kernelized Correlation Filter outperformed various other trackers in varying environmental scenarios, with a combined success rate and a tracking accuracy of 0.8041. This approach was further implemented in real-time on a Jetson Orion Nano GPU, utilizing a pan-tilt gimbal and an Intel RealSense D435i camera. Running at 20 FPS, the system demonstrated robustness and stability during motion and various environmental scenarios, highlighting its potential for integration into applications such as ground-based UAV surveillance.