1. CAROM - Vehicle Localization and Traffic Scene Reconstruction from Monocular Cameras on Road Infrastructures
- Author
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Yezhou Yang, Duo Lu, Varun C Jammula, Jeffrey Wishart, Yan Chen, and Steven Como
- Subjects
FOS: Computer and information sciences ,Monocular ,Vehicle tracking system ,Computer science ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Location awareness ,Benchmarking ,computer.software_genre ,Data structure ,Drone ,Computer Science - Robotics ,ComputerSystemsOrganization_MISCELLANEOUS ,Gps data ,Traffic scene ,Computer vision ,Artificial intelligence ,business ,Robotics (cs.RO) ,computer - Abstract
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., Comment: Accepted to IEEE ICRA 2021
- Published
- 2021
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