--- license: cc-by-4.0 task_categories: - keypoint-detection - image-feature-extraction language: - en pretty_name: Stabilo Benchmark Data size_categories: - 1K **📦 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. ![Consensus capture: unmasked ORB follows the moving vehicles, the same pipeline under oriented-box masks follows the ground](https://raw.githubusercontent.com/rfonod/stabilo-benchmark/main/assets/consensus_capture.png) 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.