CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos
This addresses the need for cost-effective traffic scene reconstruction for road safety regulators, city planners, researchers, and autonomous driving developers, offering a practical alternative to infrastructure-mounted cameras.
The paper tackles the problem of reconstructing traffic scenes from aerial videos by developing a method that processes these videos into vehicle trajectory data, achieving an average localization error of 0.1 m to 0.3 m using a consumer-grade drone at 120 meters, and compiling a dataset of 50 reconstructed scenes from 100 hours of video.
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.