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arXiv 2104.00893cs.RO
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CAROM——基于道路基础设施单目摄像头的车辆定位与交通场景重建

CAROM -- Vehicle Localization and Traffic Scene Reconstruction from Monocular Cameras on Road Infrastructures

Arizona State University(亚利桑那州立大学);Exponent(Exponent公司)

Duo Lu, Varun C Jammula, Steven Como, Jeffrey Wishart, Yan Chen, Yezhou Yang

AI总结:

本文提出CAROM框架,利用道路基础设施单目摄像头实现车辆定位与交通场景重建,并通过包含GPS、路侧视频和无人机视频的基准数据集验证,在50米和120米范围内定位误差分别约为0.8米和1.7米。

摘要:

交通监控摄像头是交通管理的强大工具,也是智能道路基础设施系统的重要组成部分。本文提出了一个使用这些摄像头的车辆定位与交通场景重建框架,称为CAROM,即“地图上的车辆”(CARs On the Map)。CAROM处理交通监控视频,并将其转换为包含车辆类型、三维形状、位置和速度的匿名数据结构,用于交通场景重建与回放。通过与美国当地交通部门合作,我们构建了一个包含GPS数据、路侧摄像头视频和无人机视频的基准数据集,以验证车辆跟踪结果。平均而言,在距离摄像头50米和120米范围内,定位误差分别约为0.8米和1.7米。

英文摘要:

Traffic monitoring cameras are powerful tools for traffic management and essential components of intelligent road infrastructure systems. In this paper, we present a vehicle localization and traffic scene reconstruction framework using these cameras, dubbed as CAROM, i.e., "CARs On the Map". CAROM processes traffic monitoring videos and converts them to anonymous data structures of vehicle type, 3D shape, position, and velocity for traffic scene reconstruction and replay. Through collaborating with a local department of transportation in the United States, we constructed a benchmarking dataset containing GPS data, roadside camera videos, and drone videos to validate the vehicle tracking results. On average, the localization error is approximately 0.8 m and 1.7 m within the range of 50 m and 120 m from the cameras, respectively.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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