# Vision-based Vehicle Speed Estimation: A Survey

arXiv: https://arxiv.org/abs/2101.06159 · HTML: https://mlsanity.com/paper/2101.06159/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 146 references that are in the map.
- First posted on arXiv 15 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [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)
- [Video action recognition for lane-change classification and prediction of surrounding vehicles](https://mlsanity.com/paper/2101.05043/) (2021)
- [A Cheap System for Vehicle Speed Detection](https://mlsanity.com/paper/1501.06751/) (2015)

## Built on by (most cited first, 11 of 11)

- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey](https://mlsanity.com/paper/2412.00348/) (2024)
- [Data-driven vehicle speed detection from synthetic driving simulator images](https://mlsanity.com/paper/2104.09903/) (2021)
- [A dataset for audio-video based vehicle speed estimation](https://mlsanity.com/paper/2212.01651/) (2022)
- [TSBOW -- Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions](https://mlsanity.com/paper/2602.05414/) (2026)
- [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)
- [FARSEC: A Reproducible Framework for Automatic Real-Time Vehicle Speed Estimation Using Traffic Cameras](https://mlsanity.com/paper/2309.14468/) (2023)
- [Probabilistic Image-Driven Traffic Modeling via Remote Sensing](https://mlsanity.com/paper/2403.05521/) (2024)
- [Estimating Vehicle Speed on Roadways Using RNNs and Transformers: A Video-based Approach](https://mlsanity.com/paper/2502.15545/) (2025)
- [Intelligent CCTV for Urban Design: AI-Based Analysis of Soft Infrastructure at Intersections](https://mlsanity.com/paper/2605.05402/) (2026)

## Abstract (arXiv)

The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits is considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision-based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision-based vehicle speed estimation. We describe the terminology, the application domains, and propose a complete taxonomy of a large selection of works that categorizes all stages involved. An overview of performance evaluation metrics and available datasets is provided. Finally, we discuss current limitations and future directions.