# Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement

arXiv: https://arxiv.org/abs/1702.06451 · HTML: https://mlsanity.com/paper/1702.06451/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2017.

## Built on by (most cited first, 17 of 17)

- [Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography](https://mlsanity.com/paper/2103.15293/) (2021)
- [Vision-based Vehicle Speed Estimation: A Survey](https://mlsanity.com/paper/2101.06159/) (2021)
- [Toward Planet-Wide Traffic Camera Calibration](https://mlsanity.com/paper/2311.04243/) (2023)
- [Camera Calibration with Pose Guidance](https://mlsanity.com/paper/2102.10202/) (2021)
- [Traffic-Net: 3D Traffic Monitoring Using a Single Camera](https://mlsanity.com/paper/2109.09165/) (2021)
- [Traffic Camera Calibration via Vehicle Vanishing Point Detection](https://mlsanity.com/paper/2103.11438/) (2021)
- [CenterLoc3D: Monocular 3D Vehicle Localization Network for Roadside Surveillance Cameras](https://mlsanity.com/paper/2203.14550/) (2022)
- [Data-driven vehicle speed detection from synthetic driving simulator images](https://mlsanity.com/paper/2104.09903/) (2021)
- [Turning Traffic Monitoring Cameras into Intelligent Sensors for Traffic Density Estimation](https://mlsanity.com/paper/2111.00941/) (2021)
- [MamV2XCalib: V2X-based Target-less Infrastructure Camera Calibration with State Space Model](https://mlsanity.com/paper/2507.23595/) (2025)
- [Towards view-invariant vehicle speed detection from driving simulator images](https://mlsanity.com/paper/2206.00343/) (2022)
- [Digital twins to alleviate the need for real field data in vision-based vehicle speed detection systems](https://mlsanity.com/paper/2407.08380/) (2024)
- [Homography Estimation in Complex Topological Scenes](https://mlsanity.com/paper/2308.01086/) (2023)
- [A Practical Large-Scale Roadside Multi-View Multi-Sensor Spatial Synchronization Framework for Intelligent Transportation Systems](https://mlsanity.com/paper/2311.04231/) (2023)
- [CVVLSNet: Vehicle Location and Speed Estimation Using Partial Connected Vehicle Trajectory Data](https://mlsanity.com/paper/2410.00132/) (2024)
- [Efficient Vision-based Vehicle Speed Estimation](https://mlsanity.com/paper/2505.01203/) (2025)
- [EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications](https://mlsanity.com/paper/2604.16783/) (2026)

## Abstract (arXiv)

In this paper, we focus on fully automatic traffic surveillance camera calibration, which we use for speed measurement of passing vehicles. We improve over a recent state-of-the-art camera calibration method for traffic surveillance based on two detected vanishing points. More importantly, we propose a novel automatic scene scale inference method. The method is based on matching bounding boxes of rendered 3D models of vehicles with detected bounding boxes in the image. The proposed method can be used from arbitrary viewpoints, since it has no constraints on camera placement. We evaluate our method on the recent comprehensive dataset for speed measurement BrnoCompSpeed. Experiments show that our automatic camera calibration method by detection of two vanishing points reduces error by 50% (mean distance ratio error reduced from 0.18 to 0.09) compared to the previous state-of-the-art method. We also show that our scene scale inference method is more precise, outperforming both state-of-the-art automatic calibration method for speed measurement (error reduction by 86% -- 7.98km/h to 1.10km/h) and manual calibration (error reduction by 19% -- 1.35km/h to 1.10km/h). We also present qualitative results of the proposed automatic camera calibration method on video sequences obtained from real surveillance cameras in various places, and under different lighting conditions (night, dawn, day).