论文标题
贸易：无人机的3D轨迹和地面深度估计的对象跟踪
TRADE: Object Tracking with 3D Trajectory and Ground Depth Estimates for UAVs
论文作者
论文摘要
我们提出了在装有单个相机的无人机中，在混乱的环境中，在杂乱的环境中进行了强大的跟踪和3D定位的贸易。最终，贸易可以使3D感知的目标追随者。 通过检测方法跟踪方法很容易受到目标切换的影响，尤其是在类似对象之间。因此，贸易预测并结合了目标3D轨迹，以从跟踪器的响应图中选择正确的目标。与静态环境不同，从单个摄像机对移动目标的深度估计是一个不适的问题。因此，我们提出了一种新型的3D定位方法，用于在复杂地形上的地面目标。它通过结合地面平面分割，深度 - 运动和单图像深度估计来结合场景几何形状的原因。使用贸易的好处被证明是在这项工作中模拟的几个动态场景上跟踪鲁棒性和深度精度。此外，我们通过在四极管的板计算机上运行贸易来证明使用热摄像机之后的自主目标。
We propose TRADE for robust tracking and 3D localization of a moving target in cluttered environments, from UAVs equipped with a single camera. Ultimately TRADE enables 3d-aware target following. Tracking-by-detection approaches are vulnerable to target switching, especially between similar objects. Thus, TRADE predicts and incorporates the target 3D trajectory to select the right target from the tracker's response map. Unlike static environments, depth estimation of a moving target from a single camera is a ill-posed problem. Therefore we propose a novel 3D localization method for ground targets on complex terrain. It reasons about scene geometry by combining ground plane segmentation, depth-from-motion and single-image depth estimation. The benefits of using TRADE are demonstrated as tracking robustness and depth accuracy on several dynamic scenes simulated in this work. Additionally, we demonstrate autonomous target following using a thermal camera by running TRADE on a quadcopter's board computer.
