# Calibration-Free Vehicle Speed Estimation: A Monocular Keypoint-Template Approach

arXiv: https://arxiv.org/abs/2608.16785 · HTML: https://mlsanity.com/paper/2608.16785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Detecting Vanishing Points using Global Image Context in a Non-Manhattan World](https://mlsanity.com/paper/1608.05684/) (2016)
- [BoxCars: Improving Fine-Grained Recognition of Vehicles using 3-D Bounding Boxes in Traffic Surveillance](https://mlsanity.com/paper/1703.00686/) (2017)
- [Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement](https://mlsanity.com/paper/1702.06441/) (2017)
- [RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking](https://mlsanity.com/paper/2507.08729/) (2025)
- [A dataset for audio-video based vehicle speed estimation](https://mlsanity.com/paper/2212.01651/) (2022)
- [Video-based Vehicle Surveillance in the Wild: License Plate, Make, and Model Recognition with Self Reflective Vision-Language Models](https://mlsanity.com/paper/2508.01387/) (2025)
- [Digital twins to alleviate the need for real field data in vision-based vehicle speed detection systems](https://mlsanity.com/paper/2407.08380/) (2024)

## Abstract (arXiv)

This paper proposes a calibration-free framework for reliably and effectively estimating vehicle speeds from monocular videos, without relying on roadway features, camera calibration, or roadway-feature-based reference objects. The proposed framework estimates vehicle speeds using a 36-keypoint vehicle template and a homography matrix updated at each frame. A YOLO-based keypoint detection module is trained on diverse datasets, and two estimation strategies are compared: keypoint-only tracking and warped optical flow with dense spatial aggregation. Speed is estimated by projecting displacements into metric space using the homography, with validation conducted on over 400 video clips from roadside and overhead datasets, covering speeds from 30 to 100 mph. The method achieves reliable speed estimation on the VS13 and BrnoCompSpeed datasets, with the warped optical flow method delivering MAEs of 15.0% and 9.7%, respectively, and 77.9% and 93.1% of estimates falling within +/-20% error. After applying a 10% trim to remove edge-of-frame outliers, performance improves to MAEs of 11.7% and 7.6%, with within-+/-20% accuracy increasing to 85.3% and 95.4%. This work addresses key limitations of existing vision-based approaches and enables low-cost and efficient speed enforcement using portable devices such as dashcams and smartphones, thereby supporting citizen-based enforcement programs for traffic safety.