Abstract
Search and rescue (SAR) operations require rapid responses to save lives or property. Unmanned Aerial Vehicles (UAVs) equipped with vision-based systems support these missions through prior terrain investigation or real-time assistance during the mission itself. Vision-based UAV frameworks aid human search tasks by detecting and recognizing specific individuals, then tracking and following them while maintaining a safe distance. A key safety requirement for UAV following is the accurate estimation of the distance between camera and target object under real-world conditions, achieved by fusing multiple image modalities. UAVs with deep learning-based vision systems offer a new approach to the planning and execution of SAR operations. As part of the system for automatic people detection and face recognition using deep learning, in this paper we present the fusion of depth camera measurements and monocular camera-to-body distance estimation for robust tracking and following. Deep learning-based filtering of depth camera data and estimation of camera-to-body distance from a monocular camera are achieved with YOLO-pose, enabling real-time fusion of depth information using the Extended Kalman Filter (EKF) algorithm. The proposed subsystem, designed for use in drones, estimates and measures the distance between the depth camera and the human body keypoints, to maintain the safe distance between the drone and the human target. Our system provides an accurate estimated distance, which has been validated against motion capture ground truth data. The system has been tested in real time indoors, where it reduces the average errors, root mean square error (RMSE) and standard deviations of distance estimation up to 15,3\% in three tested scenarios.
Abstract (translated)
搜索和救援（SAR）行动要求快速响应以拯救生命或财产。装备有基于视觉系统的无人飞行器（UAVs）支持通过预先的地形调查或在任务本身中实时协助来执行这些任务。基于视觉的无人机框架通过检测并识别特定个体，然后跟踪和跟随他们，同时保持安全距离，从而帮助人类搜索任务。无人机跟随的关键安全性要求是在真实条件下准确估计相机与目标对象之间的距离，这是通过融合多种图像模态实现的。装备有基于深度学习的视觉系统的无人机为SAR操作的规划和执行提供了一种新方法。 本文中，在自动人员检测和面部识别的系统框架内，我们提出了一种结合深度摄像头测量数据和单目摄像头到人体的距离估算的方法，以实现稳健跟踪和跟随。通过YOLO-pose实现了基于深度学习的深度摄像头数据过滤以及从单目摄像头估计相机与人体之间的距离，从而能够使用扩展卡尔曼滤波器（EKF）算法实时融合深度信息。 我们设计的子系统旨在用于无人机，并且可以估算并测量深度摄像机与人类关键点之间和人体之间的距离，以维持无人机与人类目标之间的安全距离。我们的系统提供了准确的距离估计，在与运动捕捉地面真实数据对比后得到了验证。在室内环境下进行实测时，该系统可以在三种测试场景中将平均误差、均方根误差（RMSE）和距离估算的标准差减少高达15.3%。 这段文字详细描述了一种利用深度学习技术来改进无人机搜索与救援任务的创新方法，并且展示了其在实际应用中的有效性。
URL
https://arxiv.org/abs/2602.20958
