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

arXiv: https://arxiv.org/abs/2104.00893 · HTML: https://mlsanity.com/paper/2104.00893/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2021.

## Built on by (most cited first, 5 of 5)

- [CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos](https://mlsanity.com/paper/2306.00075/) (2023)
- [MC-BEVRO: Multi-Camera Bird Eye View Road Occupancy Detection for Traffic Monitoring](https://mlsanity.com/paper/2502.11287/) (2025)
- [SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras](https://mlsanity.com/paper/2309.01324/) (2023)
- [A Practical Large-Scale Roadside Multi-View Multi-Sensor Spatial Synchronization Framework for Intelligent Transportation Systems](https://mlsanity.com/paper/2311.04231/) (2023)
- [Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks](https://mlsanity.com/paper/2409.00622/) (2024)

## Abstract (arXiv)

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.