| --- |
| license: cc-by-4.0 |
| task_categories: |
| - keypoint-detection |
| - image-feature-extraction |
| language: |
| - en |
| pretty_name: Stabilo Benchmark Data |
| size_categories: |
| - 1K<n<10K |
| annotations_creators: |
| - machine-generated |
| - expert-generated |
| viewer: false |
| tags: |
| - image |
| - benchmark |
| - image-registration |
| - homography-estimation |
| - feature-matching |
| - video-stabilization |
| - ransac |
| - aerial-imagery |
| - drone |
| - uav |
| - birds-eye-view |
| - oriented-bounding-box |
| - ground-control-points |
| - urban-traffic |
| - songdo |
| - stabilo |
| - hbb2obb |
| - arxiv:2611.17190 |
| --- |
| |
| # Stabilo Benchmark Data: Reference-Based UAV Video Stabilization on Dynamic Urban Scenes |
|
|
| [](https://doi.org/10.5281/zenodo.23623540) |
| [](https://arxiv.org/abs/2611.17190) |
| [](https://github.com/rfonod/stabilo-benchmark) |
| [](https://pypi.org/project/stabilo-benchmark/) |
| [](LICENSE) |
| [](https://huggingface.co/datasets/rfonod/songdo-vision-obb) |
|
|
| The data and every result of the paper **"Consensus Capture in Reference-Based UAV Video |
| Stabilization: A Benchmark on Dynamic Urban Scenes"** |
| ([arXiv:2611.17190](https://arxiv.org/abs/2611.17190)). The benchmark evaluates the complete |
| registration pipeline (detector, descriptor, matcher, robust estimator and object masking) for |
| reference-based stabilization of hovering-drone video, where every frame is registered onto one |
| reference frame. Its question is whether vehicles moving together can capture the robust |
| estimator's consensus: a homography fitted to such a group is internally consistent, so inlier |
| statistics cannot reveal it, and the benchmark measures alignment from outside the estimator. |
|
|
|  |
|
|
| > **📦 The data is hosted on Zenodo, not on Hugging Face.** |
| > This page is a documentation mirror and index card. The full record (≈6.2 GB) lives immutably on |
| > Zenodo; this card describes v1, DOI |
| > [`10.5281/zenodo.23623540`](https://doi.org/10.5281/zenodo.23623540). It is read by the |
| > [stabilo-benchmark](https://github.com/rfonod/stabilo-benchmark) software, which downloads, |
| > verifies and extracts it in one command (see [Access the data](#access-the-data)). |
|
|
| ## Data at a glance |
|
|
| | Property | Value | |
| |---|---| |
| | Part I (static) | 29 4K aerial frames (3840 × 2160) with vehicle boxes, warped by synthetic homographies that are known exactly | |
| | Part II (dynamic) | 9 evaluation and 5 tuning cases, each one drone hover: a survey-chosen reference frame and 135 later frames, from 1 frame to two minutes later | |
| | Frames | 1,933 (29 static, 1,224 evaluation, 680 tuning) | |
| | Vehicle boxes | 106,921, each as a horizontal and an oriented box with a conversion score (machine-generated with [hbb2obb](https://github.com/rfonod/hbb2obb)) | |
| | Ground control points | 15 per reference frame, placed by hand, and 12 to 15 per audited frame, proposed by an image fit and reviewed by eye; eight audited frames per evaluation case | |
| | Results | Every campaign of the paper: per-trial and per-pair records, manifests, the reference estimator's cache and audit, and the CSV files the paper names | |
| | Classes | 4: `0` car, `1` bus, `2` truck, `3` motorcycle | |
| | Acquisition | KAIST–EPFL multi-drone experiment, 10 drones over 20 intersections, 140 to 150 m, 4K at 29.97 FPS | |
| | Location and dates | Songdo International Business District, South Korea, October 6 and 7, 2022 | |
| | Total size | ≈6.2 GB in five ZIP archives | |
| | License | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) | |
|
|
| ## The two parts |
|
|
| **Part I, static scenes.** Each frame is warped by a homography drawn from a known distribution, |
| with photometric degradation, and every pipeline is asked to recover it. The ground truth is |
| exact, and the perturbation of a given trial does not depend on the pipeline, so the comparison is |
| paired. The 29 frames are the first frame of the first hover of every drone-intersection pair in |
| one session, a census rather than a selection, and this record is their first publication. |
| Tuning and evaluation are split by site (9 and 20 frames). |
|
|
| **Part II, dynamic cases.** Real hovers, selected from the video alone by the amount and the |
| coherence of moving foreground (a 2 × 2 design with two cases per cell, plus a stationary control). |
| Candidates are scored against a pseudo-ground-truth reference estimator whose error is audited with |
| the ground control points, which also score every candidate directly. |
|
|
|  |
|
|
| The vehicle boxes are detections, not human annotations: they serve masking and the object-level |
| metrics, where every pipeline receives the identical mask. They are not meant for training or |
| evaluating a detector; for that, see [Songdo Vision](https://huggingface.co/datasets/rfonod/songdo-vision) |
| and [Songdo Vision OBB](https://huggingface.co/datasets/rfonod/songdo-vision-obb). |
|
|
| ## Access the data |
|
|
| The record is published and versioned on Zenodo. Please download it from the canonical source; |
| **it is not redistributed on Hugging Face**: |
|
|
| ➡️ **v1:** DOI [`10.5281/zenodo.23623540`](https://doi.org/10.5281/zenodo.23623540) |
|
|
| **With the benchmark software**, which also regenerates every benchmark table and figure of the |
| paper from the stored results: |
|
|
| ```bash |
| git clone https://github.com/rfonod/stabilo-benchmark.git && cd stabilo-benchmark |
| python3.11 -m venv .venv && source .venv/bin/activate |
| pip install -e . |
| stabilo-benchmark fetch songdo # downloads, verifies and extracts into data/songdo/ |
| ``` |
|
|
| **Or with [`zenodo_get`](https://github.com/dvolgyes/zenodo_get):** |
|
|
| ```bash |
| pip install zenodo_get |
| zenodo_get 10.5281/zenodo.23623540 |
| sha256sum -c SHA256SUMS |
| ``` |
|
|
| **Files on Zenodo:** |
|
|
| | File | Size | Contents | |
| |---|---|---| |
| | `static.zip` | ≈0.31 GB | Part I: frames, boxes, provenance and the Part I campaigns | |
| | `dynamic.zip` | ≈3.72 GB | Part II evaluation cases with ground control points, the reference estimator's audit and campaigns, and the case survey | |
| | `dynamic_tuning.zip` | ≈2.17 GB | Part II tuning cases with their campaigns, and their survey | |
| | `supplementary.zip` | small | The CSV files the paper names, with an index, and the scripts of its side analyses | |
| | `configs.zip` | small | The campaign configs, the scene split and the case lists | |
| | `README.md` | small | Full documentation of the record | |
| | `LICENSE.txt` | small | CC BY 4.0 terms | |
| | `SHA256SUMS` | small | SHA-256 of every other file | |
|
|
| The record's [README](https://doi.org/10.5281/zenodo.23623540) documents the case selection, the |
| directory layout, the annotation commands, the ground control point protocol, the result files and |
| the known limitations. |
|
|
| ## Related datasets and resources |
|
|
| - **stabilo-benchmark**: the benchmark software this record accompanies: |
| [github.com/rfonod/stabilo-benchmark](https://github.com/rfonod/stabilo-benchmark) · v1.0.0, |
| [`10.5281/zenodo.24168256`](https://doi.org/10.5281/zenodo.24168256) |
| - **Songdo Vision OBB**: oriented vehicle boxes for 5,419 frames of the same experiment, introduced by the |
| same paper, whose grid search selected the conversion settings used here: |
| HF [`rfonod/songdo-vision-obb`](https://huggingface.co/datasets/rfonod/songdo-vision-obb) · |
| [`10.5281/zenodo.15050578`](https://doi.org/10.5281/zenodo.15050578) |
| - **Songdo Vision**: human-verified horizontal vehicle boxes for the same 5,419 frames: |
| HF [`rfonod/songdo-vision`](https://huggingface.co/datasets/rfonod/songdo-vision) · v2, |
| [`10.5281/zenodo.21728430`](https://doi.org/10.5281/zenodo.21728430) |
| - **Songdo Traffic**: georeferenced vehicle trajectories from the same experiment, used to check the |
| case selection: HF [`rfonod/songdo-traffic`](https://huggingface.co/datasets/rfonod/songdo-traffic) · |
| v2, [`10.5281/zenodo.17924857`](https://doi.org/10.5281/zenodo.17924857) |
| - **Stabilo**: the stabilization library the pipelines run through: |
| [github.com/rfonod/stabilo](https://github.com/rfonod/stabilo) |
| - **HBB2OBB**: the annotation tool: [github.com/rfonod/hbb2obb](https://github.com/rfonod/hbb2obb) |
| - **Geo-trax detector**: the source of the detector weights (CC BY 4.0): HF model |
| [`rfonod/geo-trax`](https://huggingface.co/rfonod/geo-trax) · |
| [github.com/rfonod/geo-trax](https://github.com/rfonod/geo-trax) |
| - **Publications**: the paper this record accompanies |
| ([arXiv:2611.17190](https://arxiv.org/abs/2611.17190)), and the article behind the imagery, |
| *Transportation Research Part C* (2025): |
| [`10.1016/j.trc.2025.105205`](https://doi.org/10.1016/j.trc.2025.105205) · |
| [arXiv:2411.02136](https://arxiv.org/abs/2411.02136) |
|
|
| ## Citation |
|
|
| If you use this data, please cite the paper it accompanies |
| ([arXiv:2611.17190](https://arxiv.org/abs/2611.17190)): |
|
|
| ```bibtex |
| @article{fonod2026benchmark, |
| author = {Fonod, Robert and Geroliminis, Nikolas}, |
| title = {Consensus Capture in Reference-Based UAV Video Stabilization: A Benchmark on Dynamic Urban Scenes}, |
| journal = {arXiv preprint arXiv:2611.17190}, |
| year = {2026}, |
| doi = {10.48550/arXiv.2611.17190} |
| } |
| ``` |
|
|
| Please also cite the article behind the imagery: |
|
|
| ```bibtex |
| @article{fonod2025advanced, |
| title = {Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery}, |
| author = {Fonod, Robert and Cho, Haechan and Yeo, Hwasoo and Geroliminis, Nikolas}, |
| journal = {Transportation Research Part C: Emerging Technologies}, |
| volume = {178}, |
| pages = {105205}, |
| year = {2025}, |
| doi = {10.1016/j.trc.2025.105205} |
| } |
| ``` |
|
|
| ## License |
|
|
| This record is released under the |
| [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) |
| license; see the [LICENSE](LICENSE) file for the full terms. You are free to share and adapt the |
| material for any purpose, provided you give appropriate credit. |
|
|
