发表机构
The University of California, Berkeley; The University of Texas, Austin（加利福尼亚大学伯克利分校; 德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种无校准的单目车辆速度估计框架，采用36关键点车辆模板与YOLO关键点检测，在VS13等数据集上实现低MAE，可用于便携式设备的低成本速度执法。
AI 中文摘要
本文提出了一种无需校准的框架，用于从单目视频中可靠且有效地估计车辆速度，该框架不依赖道路特征、相机校准或基于道路特征的参考对象。所提框架使用36个关键点的车辆模板和每帧更新的单应性矩阵来估计车辆速度。基于YOLO的关键点检测模块在多样化数据集上进行训练，并比较了两种估计策略：仅关键点跟踪和带密集空间聚合的变形光流。通过使用单应性将位移投影到度量空间来估计速度，并在来自路边和 overhead 数据集的400多个视频片段上进行验证，覆盖速度范围为30至100 mph。该方法在VS13和BrnoCompSpeed数据集上实现了可靠的速度估计，其中变形光流方法的平均绝对误差（MAE）分别为15.0%和9.7%，且分别有77.9%和93.1%的估计值落在±20%误差范围内。在应用10%的修剪以去除帧边缘异常值后，性能提升至MAE分别为11.7%和7.6%，±20%误差范围内的准确率分别提高至85.3%和95.4%。本研究解决了现有基于视觉方法的关键局限，并支持使用行车记录仪和智能手机等便携式设备实现低成本、高效的速度执法，从而支持基于公民的交通安全执法项目。
英文摘要
This paper proposes a calibration-free framework for reliably and effectively estimating vehicle speeds from monocular videos, without relying on roadway features, camera calibration, or roadway-feature-based reference objects. The proposed framework estimates vehicle speeds using a 36-keypoint vehicle template and a homography matrix updated at each frame. A YOLO-based keypoint detection module is trained on diverse datasets, and two estimation strategies are compared: keypoint-only tracking and warped optical flow with dense spatial aggregation. Speed is estimated by projecting displacements into metric space using the homography, with validation conducted on over 400 video clips from roadside and overhead datasets, covering speeds from 30 to 100 mph. The method achieves reliable speed estimation on the VS13 and BrnoCompSpeed datasets, with the warped optical flow method delivering MAEs of 15.0% and 9.7%, respectively, and 77.9% and 93.1% of estimates falling within +/-20% error. After applying a 10% trim to remove edge-of-frame outliers, performance improves to MAEs of 11.7% and 7.6%, with within-+/-20% accuracy increasing to 85.3% and 95.4%. This work addresses key limitations of existing vision-based approaches and enables low-cost and efficient speed enforcement using portable devices such as dashcams and smartphones, thereby supporting citizen-based enforcement programs for traffic safety.
Comments19 pages, 7 figures
