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Tracking a moving target using autonomy remains a critical challenge for UAVs, especially relying solely on visual input. This paper presents a vision-based target tracking framework that integrates YOLO for real-time object detection with the DRL algorithm SAC, suitable for continuous control tasks. The YOLO model provides fast and accurate target detection and localization, serving as the input observation for SAC agent. Entropy regularization of SAC enables the UAV to perform smooth and stable maneuvers in continuous action spaces. All the training and evaluation is conducted in the Airsim simulation environment, which closely replicates real-world conditions. Experimental results demonstrate that the YOLO-SAC framework effectively learns to track the moving target in complex environment by only using visual information. Furthermore, the proposed framework is compared with other DRL algorithms to highlight its robustness and superior performance.

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UAV Target Tracking in Dynamic Environment Using Deep Reinforcement Learning: A Vision-Based Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Talha Munir Rana,
  • Shuying Wu,
  • Lirong Wu,
  • Hang Yin,
  • Han Luo,
  • Boyang Zhang,
  • Bo Li

摘要

Tracking a moving target using autonomy remains a critical challenge for UAVs, especially relying solely on visual input. This paper presents a vision-based target tracking framework that integrates YOLO for real-time object detection with the DRL algorithm SAC, suitable for continuous control tasks. The YOLO model provides fast and accurate target detection and localization, serving as the input observation for SAC agent. Entropy regularization of SAC enables the UAV to perform smooth and stable maneuvers in continuous action spaces. All the training and evaluation is conducted in the Airsim simulation environment, which closely replicates real-world conditions. Experimental results demonstrate that the YOLO-SAC framework effectively learns to track the moving target in complex environment by only using visual information. Furthermore, the proposed framework is compared with other DRL algorithms to highlight its robustness and superior performance.