The paper proposes a novel approach to enhance human tracking by Unmanned Aerial Vehicles (UAVs) by integrating the YOLOv8 object detection model for human detection and a Linear Kalman Filter for trajectory planning. By combining these techniques, the system aims to improve the accuracy and reliability of monitoring individuals and predicting their movements in dynamic environments. The methodology involves utilizing YOLOv8 for high-precision human detection and applying the Kalman Filter to predict accurate states based on noisy sensor measurements and uncertain prediction equations. Experimental results from simulations and real-world testing demonstrate the effectiveness of the proposed approach in achieving precise human tracking by a UAS.