# Mobile Traffic Camera Calibration from Road Geometry for UAV-Based Traffic Surveillance

arXiv: https://arxiv.org/abs/2605.11900 · HTML: https://mlsanity.com/paper/2605.11900/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 12 May 2026.

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

- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [Automated Camera Calibration via Homography Estimation with GNNs](https://mlsanity.com/paper/2311.02598/) (2023)
- [Hybrid Visual Telemetry for Bandwidth-Constrained Robotic Vision: A Pilot Study with HEVC Base Video and JPEG ROI Stills](https://mlsanity.com/paper/2605.01826/) (2026)

## Built on by (most cited first, 1 of 1)

- [SASGeo: Stability-Aware Semantic Map Localization for GNSS-Denied UAVs -- A Framework and Synthetic Proof of Concept](https://mlsanity.com/paper/2607.07737/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) can provide flexible traffic surveillance where fixed roadside cameras are unavailable, costly, or impractical. However, raw UAV video is difficult to use for traffic analytics because vehicle motion is observed in perspective image coordinates rather than in a stable metric road coordinate system. This paper presents a lightweight pipeline for converting monocular oblique UAV traffic video into a local metric bird's-eye-view (BEV) representation. Visible road geometry, including lane markings, road borders, and crosswalks, is used to estimate a road-plane homography from image coordinates to metric ground-plane coordinates. Vehicle observations from dataset annotations or detectors are then projected to BEV using estimated ground contact points. The resulting trajectories support estimation of vehicle direction, speed, heading, and dynamic 3D cuboids on the road plane. We evaluate the pipeline on UAVDT using ground-truth annotations to isolate calibration and geometric reconstruction from detector and tracker errors. For sequence M1401, 40 sampled frames from img000001-img000196 produce 632 metric cuboid instances across 23 tracks. Results show that road-geometry calibration can transform monocular UAV footage into interpretable traffic-camera-style analytics, including BEV tracks and synchronized 3D cuboid visualizations. They also reveal key limitations: far-field vehicles are sensitive to homography errors, manual validation is currently more reliable than fully automatic calibration, and the single-plane assumption limits performance in non-planar or ambiguous road regions. The proposed pipeline provides a practical foundation for deployable UAV traffic cameras and future real-time traffic digital-twin systems.