This paper presents a framework for extracting georeferenced vehicle trajectories from high-altitude drone footage, addressing key challenges in urban traffic monitoring and limitations of traditional ground-based systems. We employ state-of-the-art computer vision and deep learning to create an end-to-end pipeline that enhances vehicle detection, tracking, and trajectory stabilization. Conducted in the Songdo International Business District, South Korea, the study used a multi-drone experiment over 20 intersections, capturing approximately 12TB of 4K video data over four days. We developed a novel track stabilization method that uses detected vehicle bounding boxes as exclusion masks during image registration, which, combined with advanced georeferencing techniques, accurately transforms vehicle coordinates into real-world geographical data. Additionally, our framework includes robust vehicle dimension estimation and detailed road segmentation for in-depth traffic analysis. The framework produced two high-quality datasets: the Songdo Traffic dataset, comprising nearly 1 million unique vehicle trajectories, and the Songdo Vision dataset, containing over 5,000 human-annotated frames with about 300,000 vehicle instances in four classes. Comparisons between drone-derived data and high-precision sensor data from an instrumented probe vehicle highlight the accuracy and consistency of our framework's extraction in dense urban settings. By publicly releasing these datasets and the pipeline source code, this work sets new benchmarks for data quality, reproducibility, and scalability in traffic research. Results demonstrate the potential of integrating drone technology with advanced computer vision for precise, cost-effective urban traffic monitoring, providing valuable resources for the research community to develop intelligent transportation systems and improve traffic management strategies.

翻译：本文提出了一种从高空无人机影像中提取地理参考车辆轨迹的框架，旨在解决城市交通监测中的关键挑战以及传统地面系统的局限性。我们采用最先进的计算机视觉与深度学习技术，构建了一个端到端的处理流程，以增强车辆检测、跟踪与轨迹稳定性。研究在韩国松岛国际商务区展开，通过多无人机实验覆盖20个交叉路口，在四天内采集了约12TB的4K视频数据。我们开发了一种新颖的轨迹稳定方法，该方法在图像配准过程中将检测到的车辆边界框用作排除掩码，结合先进的地理参考技术，将车辆坐标精确转换为真实世界的地理数据。此外，我们的框架包含稳健的车辆尺寸估计与详细的道路分割，以支持深入的交通分析。该框架生成了两个高质量数据集：包含近100万条独特车辆轨迹的松岛交通数据集，以及包含5000多帧人工标注图像、约30万个车辆实例（分为四类）的松岛视觉数据集。通过将无人机提取数据与装备高精度传感器的探测车数据进行对比，凸显了本框架在密集城市环境中数据提取的准确性与一致性。通过公开这些数据集及流程源代码，本研究为交通研究领域的数据质量、可复现性与可扩展性设立了新基准。结果表明，将无人机技术与先进计算机视觉相结合，能够实现精确且经济高效的城市交通监测，为研究社区开发智能交通系统和完善交通管理策略提供了宝贵资源。