# A dataset for audio-video based vehicle speed estimation

arXiv: https://arxiv.org/abs/2212.01651 · HTML: https://mlsanity.com/paper/2212.01651/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 3 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [The 5th AI City Challenge](https://mlsanity.com/paper/2104.12233/) (2021)
- [Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement](https://mlsanity.com/paper/1702.06441/) (2017)
- [Vision-based Vehicle Speed Estimation: A Survey](https://mlsanity.com/paper/2101.06159/) (2021)
- [Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey](https://mlsanity.com/paper/1910.04656/) (2019)
- [An approach to improving sound-based vehicle speed estimation](https://mlsanity.com/paper/2204.05082/) (2022)
- [Mel-spectrogram features for acoustic vehicle detection and speed estimation](https://mlsanity.com/paper/2204.04013/) (2022)

## Built on by (most cited first, 2 of 2)

- [Digital twins to alleviate the need for real field data in vision-based vehicle speed detection systems](https://mlsanity.com/paper/2407.08380/) (2024)
- [Calibration-Free Vehicle Speed Estimation: A Monocular Keypoint-Template Approach](https://mlsanity.com/paper/2608.16785/) (2026)

## Abstract (arXiv)

Accurate speed estimation of road vehicles is important for several reasons. One is speed limit enforcement, which represents a crucial tool in decreasing traffic accidents and fatalities. Compared with other research areas and domains, the number of available datasets for vehicle speed estimation is still very limited. We present a dataset of on-road audio-video recordings of single vehicles passing by a camera at known speeds, maintained stable by the on-board cruise control. The dataset contains thirteen vehicles, selected to be as diverse as possible in terms of manufacturer, production year, engine type, power and transmission, resulting in a total of  400  annotated audio-video recordings. The dataset is fully available and intended as a public benchmark to facilitate research in audio-video vehicle speed estimation. In addition to the dataset, we propose a cross-validation strategy which can be used in a machine learning model for vehicle speed estimation. Two approaches to training-validation split of the dataset are proposed.