CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos
CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos
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DOI：
10.1109/icra48891.2023.10160502
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Duo Lu;E. Eaton;M. Weg;Wei Wang;S. Como;J. Wishart;Hongbin Yu;Yezhou Yang
中科院分区：
文献类型：
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作者：
Duo Lu;E. Eaton;M. Weg;Wei Wang;S. Como;J. Wishart;Hongbin Yu;Yezhou Yang
从视频中重建道路交通场景一直是道路安全监管机构、城市规划者、研究人员和自动驾驶技术开发人员所期望的。然而，用安装在道路基础设施上的摄像机覆盖道路的每一英里是昂贵的且不必要的。本文提出了一种将航拍视频处理成车辆轨迹数据的方法，从而实现了交通场景的自动重建和计算机精确再现。平均而言，使用120米飞行的消费级无人机，车辆定位误差约为0.1米至0.3米。该项目还从约100小时的空中视频中重建了50个道路交通场景，以支持各种下游交通分析应用程序，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上获得。
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.