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

TL;DR

CAROM is a framework that uses monocular traffic cameras to localize vehicles and reconstruct traffic scenes, providing accurate, anonymous data for traffic management and infrastructure analysis.

Contribution

The paper introduces CAROM, a novel system for vehicle localization and traffic scene reconstruction from monocular cameras, validated with a new benchmarking dataset.

Findings

Localization error of approximately 0.8 m within 50 m range

Localization error of approximately 1.7 m within 120 m range

Validated system with real-world traffic data and drone videos

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.