Title: CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos Show Chinese title

Title: CAROM Air -- 从航拍视频中进行车辆定位和交通场景重建

Abstract: Road traffic scene reconstruction from videos has been desirable by road safety regulators, city planners, researchers, and autonomous driving technology developers. However, it is expensive and unnecessary to cover every mile of the road with cameras mounted on the road infrastructure. This paper presents a method that can process aerial videos to vehicle trajectory data so that a traffic scene can be automatically reconstructed and accurately re-simulated using computers. On average, the vehicle localization error is about 0.1 m to 0.3 m using a consumer-grade drone flying at 120 meters. This project also compiles a dataset of 50 reconstructed road traffic scenes from about 100 hours of aerial videos to enable various downstream traffic analysis applications and facilitate further road traffic related research. The dataset is available at https://github.com/duolu/CAROM. Show Chinese abstract

Abstract: 从视频中重建道路交通场景一直受到道路安全监管机构、城市规划者、研究人员和自动驾驶技术开发者的关注。 然而，将道路上的每一英里都用安装在道路基础设施上的摄像头覆盖是昂贵且不必要的。 本文提出了一种方法，可以将航拍视频处理为车辆轨迹数据，从而使用计算机自动重建并准确重新模拟交通场景。 平均而言，使用一架在120米高度飞行的消费级无人机，车辆定位误差约为0.1米到0.3米。 该项目还整理了一个数据集，包含约100小时航拍视频重建的50个道路交通场景，以支持各种下游交通分析应用并促进进一步的道路交通相关研究。 该数据集可在 https://github.com/duolu/CAROM 获取。