In this paper a multiple object detection, recognition, and tracking system for unmanned aerial vehicles (UAVs) has been studied. The system can be implemented on any UAVs platform, with the main requirement being that the UAV has a suitable onboard computational unit and a camera. It is intended to be used in a maritime object tracking system framework for UAVs, which enables a UAV to perform multiobject tracking and situational awareness of the sea surface, in real time, during a UAV operation. Using machine vision to automatically detect objects in the camera's image stream combined with the UAV's navigation data, the onboard computer is able to georeference each object detection to measure the location of the detected objects in a local North‐East (NE) coordinate frame. A tracking algorithm which uses a Kalman filter and a constant velocity motion model utilizes an object's position measurements, automatically found using the object detection algorithm, to track and estimate an object's position and velocity. Furthermore, a global‐nearest‐neighbor algorithm is applied for data association. This is achieved using a measure of distance that is based not only on the physical distance between an object's estimated position and the measured position, but also how similar the objects appear in the camera image. Four field tests were conducted at sea to verify the object detection and tracking system. One of the flight tests was a two‐object tracking scenario, which is also used in three scenarios with an additional two simulated objects. The tracking results demonstrate the effectiveness of using visual recognition for data association to avoid interchanging the two estimated object trajectories. Furthermore, real‐time computations performed on the gathered data show that the system is able to automatically detect and track the position and velocity of a boat. Given that the system had at least 100 georeferenced measurements of the boat's position, the position was estimated and tracked with an accuracy of 5–15 m from 400 m altitude while the boat was in the camera's field of view (FOV). The estimated speed and course would also converge to the object's true trajectories (measured by Global Positioning System, GPS) for the tested scenarios. This enables the system to track boats while they are outside the FOV of the camera for extended periods of time, with tracking results showing a drift in the boat's position estimate down to 1–5 m/min outside of the FOV of the camera.

中文翻译：

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使用热像仪从无人机进行物体检测，识别和跟踪

本文研究了一种用于无人机的多目标检测，识别和跟踪系统。该系统可以在任何无人机平台上实施，主要要求是无人机必须具有合适的机载计算单元和摄像头。它旨在用于无人机的海上目标跟踪系统框架中，该框架使无人机能够在无人机运行期间实时执行多目标跟踪和对海面的态势感知。利用机器视觉自动检测相机图像流中的对象以及无人机的导航数据，车载计算机可以对每个对象检测进行地理定位，以测量检测到的对象在本地东北（NE）坐标系中的位置。使用卡尔曼滤波器和等速运动模型的跟踪算法利用对象的位置测量值（使用对象检测算法自动找到）来跟踪和估计对象的位置和速度。此外，全局最近邻居算法适用于数据关联。这可以通过距离的测量来实现，该距离不仅基于对象的估计位置和测量位置之间的物理距离，而且还基于对象在相机图像中出现的相似程度。在海上进行了四次现场测试，以验证物体检测和跟踪系统。飞行测试之一是两个对象的跟踪方案，该方案也用于另外两个模拟对象的三个方案。跟踪结果证明了使用视觉识别进行数据关联以避免交换两个估计的对象轨迹的有效性。此外，对收集到的数据进行的实时计算表明，该系统能够自动检测并跟踪船的位置和速度。假设系统至少对船的位置进行了100次地理参考测量，则当船处于摄像机视场（FOV）时，可以从400 m高度以5–15 m的精度估算和跟踪位置。对于测试场景，估计的速度和航向也将收敛到对象的真实轨迹（由全球定位系统GPS测量）。这样一来，系统就可以在船只长时间不在摄像机视场范围内时跟踪船只，