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

arXiv: https://arxiv.org/abs/2306.00075 · HTML: https://mlsanity.com/paper/2306.00075/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 62 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 19 of 19)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving](https://mlsanity.com/paper/2001.03343/) (2020)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019)
- [AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection](https://mlsanity.com/paper/2108.11127/) (2021)
- [Viewpoints and Keypoints](https://mlsanity.com/paper/1411.6067/) (2014)
- [OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association](https://mlsanity.com/paper/2103.02440/) (2021)
- [CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins](https://mlsanity.com/paper/2208.11036/) (2022)
- [A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification](https://mlsanity.com/paper/1905.03397/) (2019)
- [openDD: A Large-Scale Roundabout Drone Dataset](https://mlsanity.com/paper/2007.08463/) (2020)
- [Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation](https://mlsanity.com/paper/1906.06789/) (2019)
- [Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras](https://mlsanity.com/paper/2206.09770/) (2022)
- [CAROM -- Vehicle Localization and Traffic Scene Reconstruction from Monocular Cameras on Road Infrastructures](https://mlsanity.com/paper/2104.00893/) (2021)
- [Optimized tour planning for drone-based urban traffic monitoring](https://mlsanity.com/paper/2007.06911/) (2020)

## Built on by (most cited first, 7 of 7)

- [eTraM: Event-based Traffic Monitoring Dataset](https://mlsanity.com/paper/2403.19976/) (2024)
- [RiskBench: A Scenario-based Benchmark for Risk Identification](https://mlsanity.com/paper/2312.01659/) (2023)
- [MC-BEVRO: Multi-Camera Bird Eye View Road Occupancy Detection for Traffic Monitoring](https://mlsanity.com/paper/2502.11287/) (2025)
- [ATARS: An Aerial Traffic Atomic Activity Recognition and Temporal Segmentation Dataset](https://mlsanity.com/paper/2503.18553/) (2025)
- [SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras](https://mlsanity.com/paper/2309.01324/) (2023)
- [Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks](https://mlsanity.com/paper/2409.00622/) (2024)
- [Querying Perception Streams with Spatial Regular Expressions](https://mlsanity.com/paper/2411.05946/) (2024)

## Abstract (arXiv)

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.