CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos

Duo Lu; Eric Eaton; Matt Weg; Wei Wang; Steven Como; Jeffrey Wishart; Hongbin Yu; Yezhou Yang

doi:10.1109/ICRA48891.2023.10160502

CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos

Duo Lu, Eric Eaton, Matt Weg, Wei Wang, Steven Como, Jeffrey Wishart, Hongbin Yu, Yezhou Yang

Engineering, Ira A. Fulton Schools of (IAFSE)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

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.

Original language	English (US)
Title of host publication	Proceedings - ICRA 2023
Subtitle of host publication	IEEE International Conference on Robotics and Automation
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	10666-10673
Number of pages	8
ISBN (Electronic)	9798350323658
DOIs	https://doi.org/10.1109/ICRA48891.2023.10160502
State	Published - 2023
Event	2023 IEEE International Conference on Robotics and Automation, ICRA 2023 - London, United Kingdom Duration: May 29 2023 → Jun 2 2023

Publication series

Name	Proceedings - IEEE International Conference on Robotics and Automation
Volume	2023-May
ISSN (Print)	1050-4729

Conference

Conference	2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Country/Territory	United Kingdom
City	London
Period	5/29/23 → 6/2/23

ASJC Scopus subject areas

Software
Control and Systems Engineering
Electrical and Electronic Engineering
Artificial Intelligence

Access to Document

10.1109/ICRA48891.2023.10160502

Cite this

Lu, D., Eaton, E., Weg, M., Wang, W., Como, S., Wishart, J., Yu, H., & Yang, Y. (2023). CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos. In Proceedings - ICRA 2023: IEEE International Conference on Robotics and Automation (pp. 10666-10673). (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2023-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA48891.2023.10160502

CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos. / Lu, Duo; Eaton, Eric; Weg, Matt et al.
Proceedings - ICRA 2023: IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2023. p. 10666-10673 (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2023-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lu, D, Eaton, E, Weg, M, Wang, W, Como, S, Wishart, J, Yu, H & Yang, Y 2023, CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos. in Proceedings - ICRA 2023: IEEE International Conference on Robotics and Automation. Proceedings - IEEE International Conference on Robotics and Automation, vol. 2023-May, Institute of Electrical and Electronics Engineers Inc., pp. 10666-10673, 2023 IEEE International Conference on Robotics and Automation, ICRA 2023, London, United Kingdom, 5/29/23. https://doi.org/10.1109/ICRA48891.2023.10160502

Lu D, Eaton E, Weg M, Wang W, Como S, Wishart J et al. CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos. In Proceedings - ICRA 2023: IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc. 2023. p. 10666-10673. (Proceedings - IEEE International Conference on Robotics and Automation). doi: 10.1109/ICRA48891.2023.10160502

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title = "CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos",

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.",

author = "Duo Lu and Eric Eaton and Matt Weg and Wei Wang and Steven Como and Jeffrey Wishart and Hongbin Yu and Yezhou Yang",

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AU - Yu, Hongbin

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AB - 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.

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CAROM Air - Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos

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