Probabilistic target detection by camera-equipped UAVs
Probabilistic target detection by camera-equipped UAVs
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DOI：
10.1109/robot.2010.5509355
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发表时间：
2010-05
期刊：
影响因子：
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通讯作者：
A. Symington;S. Waharte;S. Julier;A. Trigoni
中科院分区：
文献类型：
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作者：
A. Symington;S. Waharte;S. Julier;A. Trigoni
本文的研究灵感来源于现实世界中无人驾驶飞行器（uav）的搜索与救援问题。本文研究了安装在四旋翼无人机底部的鸟瞰相机对静态目标的跟踪问题。我们首先提出了一个目标检测算法，然后我们在从四个不同的实验中获得的视频帧集合上执行。我们展示了目标检测算法的有效性如何随着高度的变化而变化。我们将这种功效总结成一个表，我们表示观察模型。然后，我们在一系列视频帧上运行目标检测算法，并使用来自观察模型的参数来更新递归贝叶斯估计器。估计器跟踪目标当前在摄像机视野内的概率，我们将其更简单地称为目标存在。在每次目标探测事件之间，无人机会改变位置，从而使传感区域发生变化。在关于无人机运动的某些假设下，新信息的比例可以近似为一个值，然后我们使用该值在估计器的每次迭代中对先验进行加权。通过一系列的实验，我们展示了未知区域先验值、无人机高度和摄像机采样率对估计器精度的影响。我们的结果表明，对于所有测试场景，没有单一的最佳采样率。我们还展示了如何使用先验作为一种机制，根据高假阳性或高假阴性概率是优选的来调整估计器。
This paper is motivated by the real world problem of search and rescue by unmanned aerial vehicles (UAVs). We consider the problem of tracking a static target from a bird's-eye view camera mounted to the underside of a quadrotor UAV. We begin by proposing a target detection algorithm, which we then execute on a collection of video frames acquired from four different experiments. We show how the efficacy of the target detection algorithm changes as a function of altitude. We summarise this efficacy into a table which we denote the observation model. We then run the target detection algorithm on a sequence of video frames and use parameters from the observation model to update a recursive Bayesian estimator. The estimator keeps track of the probability that a target is currently in view of the camera, which we refer to more simply as target presence. Between each target detection event the UAV changes position and so the sensing region changes. Under certain assumptions regarding the movement of the UAV, the proportion of new information may be approximated to a value, which we then use to weight the prior in each iteration of the estimator. Through a series of experiments we show how the value of the prior for unseen regions, the altitude of the UAV and the camera sampling rate affect the accuracy of the estimator. Our results indicate that there is no single optimal sampling rate for all tested scenarios. We also show how the prior may be used as a mechanism for tuning the estimator according to whether a high false positive or high false negative probability is preferable.