用道路几何信息将无人机视频转为可分析的鸟瞰图，实现交通监控
Mobile Traffic Camera Calibration from Road Geometry for UAV-Based Traffic Surveillance
- 利用车道线等道路特征估计图像到地面的投影变换
- 在M1401序列中生成632个带度量的车辆3D立方体实例
- 适合需要轻量化部署的无人机交通监测系统
无人飞行器（UAV）可在固定路侧摄像头难以覆盖或成本过高的区域提供灵活的交通监控。然而，原始无人机视频中的车辆运动以透视图像坐标呈现，难以直接用于交通分析。本文提出一种轻量级流程，将单目斜视无人机交通视频转换为局部度量的鸟瞰图（BEV）表示。通过可见道路几何特征（如车道线、路缘和人行横道）估计图像坐标到度量地面坐标的道路平面单应性变换，并基于检测器标注的车辆位置投影其接触地面点至BEV空间。由此生成的轨迹支持车辆方向、速度、航向及道路上的动态3D立方体建模。我们在UAVDT数据集上使用真实标注评估该流程，隔离校准与几何重建误差。在序列M1401中，从img000001-img000196抽取40帧，生成23条轨迹共632个度量立方体实例。结果表明，基于道路几何的校准可将单目无人机视频转化为类似交通摄像头的可解释分析结果，包括鸟瞰轨迹与同步3D立方体可视化。但同时也揭示关键局限：远距离车辆对单应性误差敏感，当前手动验证仍比全自动校准更可靠，且单平面假设限制了非平面或模糊道路区域的表现。所提流程为可部署的无人机交通摄像头及未来实时交通数字孪生系统提供了实用基础。
原文摘要 · Abstract (English)
Unmanned aerial vehicles (UAVs) can provide flexible traffic surveillance where fixed roadside cameras are unavailable, costly, or impractical. However, raw UAV video is difficult to use for traffic analytics because vehicle motion is observed in perspective image coordinates rather than in a stable metric road coordinate system. This paper presents a lightweight pipeline for converting monocular oblique UAV traffic video into a local metric bird's-eye-view (BEV) representation. Visible road geometry, including lane markings, road borders, and crosswalks, is used to estimate a road-plane homography from image coordinates to metric ground-plane coordinates. Vehicle observations from dataset annotations or detectors are then projected to BEV using estimated ground contact points. The resulting trajectories support estimation of vehicle direction, speed, heading, and dynamic 3D cuboids on the road plane. We evaluate the pipeline on UAVDT using ground-truth annotations to isolate calibration and geometric reconstruction from detector and tracker errors. For sequence M1401, 40 sampled frames from img000001-img000196 produce 632 metric cuboid instances across 23 tracks. Results show that road-geometry calibration can transform monocular UAV footage into interpretable traffic-camera-style analytics, including BEV tracks and synchronized 3D cuboid visualizations. They also reveal key limitations: far-field vehicles are sensitive to homography errors, manual validation is currently more reliable than fully automatic calibration, and the single-plane assumption limits performance in non-planar or ambiguous road regions. The proposed pipeline provides a practical foundation for deployable UAV traffic cameras and future real-time traffic digital-twin systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。