High-Resolution Vehicle Trajectory Extraction and Denoising From Aerial Videos
High-Resolution Vehicle Trajectory Extraction and Denoising From Aerial Videos
复制标题
从航拍视频中提取高分辨率车辆轨迹并进行去噪
DOI：
10.1109/tits.2020.3003782
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发表时间：
2021-05-01
影响因子：
8.5
通讯作者：
Ke, Ruimin
中科院分区：
文献类型：
--
作者：
Chen, Xinqiang;Li, Zhibin;Ke, Ruimin
近年来，无人机（UAV）以其低成本、高分辨率、灵活性好、空间覆盖范围广等优势，成为高速公路交通监控和数据采集的重要工具。从无人机航拍视频中提取高分辨率的车辆轨迹数据是交通流建模和分析的关键研究任务。本研究的目的是提出一种新的方法框架，自动和准确的车辆轨迹提取航空视频。该方法首先通过开发一个合奏检测器来检测目标区域中的车辆。然后，应用核相关滤波器对车辆进行快速、准确的跟踪。在此基础上，提出了一种映射算法，将车辆位置从图像中的笛卡尔坐标转换到Frenet坐标，以提取原始车辆轨迹沿着道路曲线。然后使用小波变换进行数据去噪，以消除有偏差的车辆轨迹位置。我们的方法进行了测试，在不同的城市快速路段在高峰和非高峰时间在工作日的两个空中视频。提取的车辆轨迹与手动校准数据进行比较，以证明框架的性能。实验结果表明，该方法成功地提取了车辆轨迹，具有较高的精度：均方差测量误差为2.301 m，均方根误差为0.175 m，Pearson相关系数为0.999。本研究中的视频和轨迹数据可在https://seutraffic.com上公开访问，作为基准。
In recent years, unmanned aerial vehicle (UAV) has become an increasingly popular tool for traffic monitoring and data collection on highways due to its advantage of low cost, high resolution, good flexibility, and wide spatial coverage. Extracting high-resolution vehicle trajectory data from aerial videos taken by a UAV flying over target highway segment becomes a critical research task for traffic flow modeling and analysis. This study aims at proposing a novel methodological framework for automatic and accurate vehicle trajectory extraction from aerial videos. The method starts by developing an ensemble detector to detect vehicles in the target region. Then, the kernelized correlation filter is applied to track vehicles fast and accurately. After that, a mapping algorithm is proposed to transform vehicle positions from the Cartesian coordinates in image to the Frenet coordinates to extract raw vehicle trajectories along the roadway curves. The data denoising is then performed using a wavelet transform to eliminate the biased vehicle trajectory positions. Our method is tested on two aerial videos taken on different urban expressway segments in both peak and non-peak hours on weekdays. The extracted vehicle trajectories are compared with manual calibrated data to testify the framework performance. The experimental results show that the proposed method successfully extracts vehicle trajectories with a high accuracy: the measurement error of Mean Squared Deviation is 2.301 m, the Root-mean-square deviation is 0.175 m, and the Pearson correlation coefficient is 0.999. The video and trajectory data in this study are publicly accessible for serving as benchmark at https://seutraffic.com.