# Toward Planet-Wide Traffic Camera Calibration

arXiv: https://arxiv.org/abs/2311.04243 · HTML: https://mlsanity.com/paper/2311.04243/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis](https://mlsanity.com/paper/1809.05782/) (2018)
- [GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision](https://mlsanity.com/paper/2007.13124/) (2020)
- [Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement](https://mlsanity.com/paper/1702.06451/) (2017)
- [Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement](https://mlsanity.com/paper/1702.06441/) (2017)
- [Traffic Camera Calibration via Vehicle Vanishing Point Detection](https://mlsanity.com/paper/2103.11438/) (2021)
- [Traffic Density Estimation using a Convolutional Neural Network](https://mlsanity.com/paper/1809.01564/) (2018)

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

- [ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones](https://mlsanity.com/paper/2406.07661/) (2024)
- [WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects under Occlusion](https://mlsanity.com/paper/2403.19022/) (2024)
- [TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes](https://mlsanity.com/paper/2412.10308/) (2024)
- [MamV2XCalib: V2X-based Target-less Infrastructure Camera Calibration with State Space Model](https://mlsanity.com/paper/2507.23595/) (2025)
- [Efficient Vision-based Vehicle Speed Estimation](https://mlsanity.com/paper/2505.01203/) (2025)
- [Dash2Sim: Closed-Loop Driving Simulation from in-the-wild Dashcam Videos](https://mlsanity.com/paper/2606.07366/) (2026)
- [RECO: Region-Aware Compensation for Extrinsic Perturbations in Roadside 3D Detection](https://mlsanity.com/paper/2607.20947/) (2026)

## Abstract (arXiv)

Despite the widespread deployment of outdoor cameras, their potential for automated analysis remains largely untapped due, in part, to calibration challenges. The absence of precise camera calibration data, including intrinsic and extrinsic parameters, hinders accurate real-world distance measurements from captured videos. To address this, we present a scalable framework that utilizes street-level imagery to reconstruct a metric 3D model, facilitating precise calibration of in-the-wild traffic cameras. Notably, our framework achieves 3D scene reconstruction and accurate localization of over 100 global traffic cameras and is scalable to any camera with sufficient street-level imagery. For evaluation, we introduce a dataset of 20 fully calibrated traffic cameras, demonstrating our method's significant enhancements over existing automatic calibration techniques. Furthermore, we highlight our approach's utility in traffic analysis by extracting insights via 3D vehicle reconstruction and speed measurement, thereby opening up the potential of using outdoor cameras for automated analysis.