# Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement

arXiv: https://arxiv.org/abs/1702.06441 · HTML: https://mlsanity.com/paper/1702.06441/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [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)
- [Robust Self-calibration of Focal Lengths from the Fundamental Matrix](https://mlsanity.com/paper/2311.16304/) (2023)
- [Traffic Camera Calibration via Vehicle Vanishing Point Detection](https://mlsanity.com/paper/2103.11438/) (2021)
- [A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecture](https://mlsanity.com/paper/2303.06202/) (2023)
- [CenterLoc3D: Monocular 3D Vehicle Localization Network for Roadside Surveillance Cameras](https://mlsanity.com/paper/2203.14550/) (2022)
- [A dataset for audio-video based vehicle speed estimation](https://mlsanity.com/paper/2212.01651/) (2022)
- [FARSEC: A Reproducible Framework for Automatic Real-Time Vehicle Speed Estimation Using Traffic Cameras](https://mlsanity.com/paper/2309.14468/) (2023)
- [Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras](https://mlsanity.com/paper/2401.07220/) (2024)
- [Efficient Vision-based Vehicle Speed Estimation](https://mlsanity.com/paper/2505.01203/) (2025)
- [Efficient Semantic Image Communication for Traffic Monitoring at the Edge](https://mlsanity.com/paper/2604.12622/) (2026)
- [Advances in Neural 3D Mesh Texturing: A Survey](https://mlsanity.com/paper/2606.00137/) (2026)
- [Calibration-Free Vehicle Speed Estimation: A Monocular Keypoint-Template Approach](https://mlsanity.com/paper/2608.16785/) (2026)

## Abstract (arXiv)

In this paper, we focus on traffic camera calibration and a visual speed measurement from a single monocular camera, which is an important task of visual traffic surveillance. Existing methods addressing this problem are difficult to compare due to a lack of a common data set with reliable ground truth. Therefore, it is not clear how the methods compare in various aspects and what factors are affecting their performance. We captured a new data set of 18 full-HD videos, each around 1 hr long, captured at six different locations. Vehicles in the videos (20865 instances in total) are annotated with the precise speed measurements from optical gates using LiDAR and verified with several reference GPS tracks. We made the data set available for download and it contains the videos and metadata (calibration, lengths of features in image, annotations, and so on) for future comparison and evaluation. Camera calibration is the most crucial part of the speed measurement; therefore, we provide a brief overview of the methods and analyze a recently published method for fully automatic camera calibration and vehicle speed measurement and report the results on this data set in detail.