论文标题
航空影像中车辆状态估计的准确性表征
Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery
论文作者
论文摘要
由于它们有能力获取航空影像，因此配备了摄像头的无人机（UAV）是获取交通信息的非常成本效益的工具。但是，对这些系统准确性的验证没有足够的关注。在本文中，对最重要的错误来源进行了分析。这包括三个关键组件。首先，通过统计过滤进行车辆状态估计。其次，对最重要的错误来源进行量化。第三，与最先进的参考传感器相比，估计状态的基准。这项工作提出了最大程度地减少最相关来源错误的方法。通过这些错误减少，配备摄像头的无人机是流量数据采集的非常吸引人的工具。测试数据和源代码公开可用。
Due to their capability of acquiring aerial imagery, camera-equipped Unmanned Aerial Vehicles (UAVs) are very cost-effective tools for acquiring traffic information. However, not enough attention has been given to the validation of the accuracy of these systems. In this paper, an analysis of the most significant sources of error is done. This includes three key components. First, a vehicle state estimation by means of statistical filtering. Second, a quantification of the most significant sources of error. Third, a benchmark of the estimated state compared with state-of-the-art reference sensors. This work presents ways to minimize the errors of the most relevant sources. With these error reductions, camera-equipped UAVs are very attractive tools for traffic data acquisition. The test data and the source code are made publicly available.
