---
license: cc-by-4.0
language:
- en
pretty_name: "Drone Maneuver Test-Clip Library"
task_categories:
- object-detection
- video-classification
tags:
- biology
- image
- animals
- CV
- drone
- UAV
- KABR
- zebra
- giraffe
- behavior
- pose
- animal-tracking
- Mpala Research Centre
size_categories:
- n<1K
description: "A library of 6-second aerial drone clips derived from the KABR dataset (Mpala Research Centre, Kenya), each indexed by the autonomous-flight maneuver it is suitable for testing, with per-frame-per-track labels (bounding box, species, behaviour, persistent track id, ground-truth pose where available, telemetry)."
---

# Dataset Card for Drone Maneuver Test-Clip Library

A benchmark of **41 six-second drone clips** (180 frames @ 30 fps)
cut from 20 KABR videos across 17 survey sessions at the Mpala Research Centre,
Kenya. Each clip is indexed by which autonomous-flight **maneuver** it can test
(launch / follow / behavior-adaptive / SoI-aware) and ships with a per-frame-per-track label table.
The library is intended for evaluating drone navigation policies against real wildlife footage.

## Dataset Details

### Dataset Description

- **Curated by:** Jenna Kline
- **Language(s) (NLP):** en
- **Homepage:** https://imageomics.github.io/wildlife-drone-maneuver/
- **Paper:** in prep
- **Related dataset:** [imageomics/KABR-mini-scene-raw-videos](https://huggingface.co/datasets/imageomics/KABR-mini-scene-raw-videos),
  [imageomics/KABR-raw-videos](https://huggingface.co/datasets/imageomics/KABR-raw-videos)

This dataset repackages the KABR aerial behavior dataset into short, mix-and-match clips that each
exercise a specific drone maneuver, so navigation policies can be benchmarked per-maneuver rather
than only end-to-end. Labels are aligned per frame per tracked individual.

### Supported Tasks and Leaderboards

Object detection / tracking, behaviour recognition, pose (viewpoint) estimation, and
**maneuver-conditioned navigation-policy evaluation** (the primary intended use).

## Dataset Structure

```
kabr_clips/
    catalog/
        video_index.csv      # per source video: frame summary + joined FAIR² metadata
        clip_index.csv       # one row per clip (the master index)
        coverage_report.md   # species x habitat x bbox-size x maneuver coverage
        pose_audit.csv       # per-video GT-pose assignment audit
    clips/
        <clip_id>/
            clip.mp4             # 6 s, 180 frames @ 30 fps
            labels.csv           # one row per frame per track
            maneuver_labels.csv  # per-frame ground-truth drone action, per maneuver
    README.md
```

The **source video** for each clip is referenced by its `fair2_video_eventID`, which resolves
in the [imageomics/kabr-full-release](https://huggingface.co/datasets/imageomics/kabr-full-release)
FAIR² dataset (`data/video_events.csv`); the session it belongs to is `fair2_session_eventID`
(`data/session_events.csv`). The catalog carries no absolute filesystem paths.

`clip_id = <date>-<video>_<start_frame>` (e.g. `18_01_2023_session_7-DJI_0070_000360`).

### Data Fields

**clips/<clip_id>/labels.csv** (one row per frame per track):
- `clip_id`, `video_id`, `session_id`: identifiers; `session_id` is the KABR/FAIR² session event.
- `frame_global`: absolute frame in the source video; `frame_local`: 0-based within the clip; `time_s`.
- `track_id`: KABR mini-scene id, **persistent within the source video**.
- `species`, `behavior`: KABR expert ground-truth labels; `vigilant`: behavior in {Head Up, Running, Trotting}.
- `pose`, `pose_provenance`, `pose_match_score`: 8-class viewpoint where ground-truth exists (see Annotations); else empty.
- `individual_id`: empty — global re-identification is future work (no ground truth exists).
- `xtl,ytl,xbr,ybr`, `x_c,y_c,w,h`, `bbox_area_frac`, `bbox_size_class` (far/medium/close, relative to this survey).
- `occluded`, `outside`: KABR annotation flags; `latitude`, `longitude`, `altitude`: drone telemetry; `date_time`.

**catalog/clip_index.csv** (one row per clip): identifiers, frame range, `species_set`, `habitat`,
`habitat_notes`, `habitat_provenance`, `herd_size`, `behaviours_present`, `has_vigilance`,
`bbox_size_classes`, `pose_set`, `suitable_maneuvers`, and the FAIR² event IDs
(`fair2_video_eventID` / `fair2_session_eventID`) that point back to the source video in
`imageomics/kabr-full-release`.

`habitat` is a structural class — **`open`**, **`closed`**, **`mixed`**, or **`unknown`** — so it can
be filtered and grouped consistently. It is derived from the original free-text field metadata
(Bitterlich relascope scores — a basal-area count, higher = denser woody canopy — plus field
remarks), which is preserved verbatim in **`habitat_notes`**. The binning is: Bitterlich 1–2 and
explicit "open" → `open`; Bitterlich 3–4, "mix", scattered/some bushes → `mixed`; Bitterlich ≥5
(e.g. 10) and "bushy" → `closed`; bare "near watering hole" / blank → `unknown`.

`habitat`/`habitat_notes` and `herd_size` are joined from the FAIR² `session_events`.
`habitat_provenance` records where each original habitat value came from: `session_event` (the
structured `habitat` field), `eventRemarks` (best-effort recovery from the free-text session remarks
where the structured field was blank), or empty (no habitat information available for that session).

**clips/<clip_id>/maneuver_labels.csv** (one row per frame per maneuver) — the **ground-truth drone
action** produced by replaying the formal maneuver decision tree over `labels.csv`:
- `maneuver`: one of `approach` / `track` / `behavior_adaptive` / `soi_aware`.
- `action_set_raw`, `action_set_smoothed`: the per-frame action(s) from the 9-action space
  (`up, down, forward, back, left, right, yaw-left, yaw-right, hover`); `smoothed` is the published
  label after a 3 s rolling average that suppresses jitter.
- `triggering_branch`: which decision-tree branch fired (for auditability).
- `S_t`, `pct_vigilant`, `centroid_x`, `centroid_y`, `mean_px`, `n_tracks`: the frame features the
  decision used, so any label is reproducible and inspectable.

### Evaluation harness (maneuver decision tree)

The accompanying replay harness (`clip_library/maneuver_labels.py`, spec in
`maneuver_decision_tree.md`) executes a small, inspectable **policy specification** deterministically
over each clip, emitting the action an expert-calibrated controller would take per frame. A learned
navigation policy can then be scored, per maneuver, against this reference. Every threshold
(vigilance `theta_S`, desired bbox pixels, SoI pose, smoothing window) is user-tunable; tuned runs
write a `maneuver_labels.custom.csv` sidecar and never overwrite the released labels. The harness is
CPU-only and runs on the released clips without the raw KABR archive.

### Composition

- Clips: **41** across **20** videos / **17** sessions.
- Maneuver coverage:
  - follow: 39 clips
  - soi_aware: 36 clips
  - behavior_adaptive: 23 clips
  - launch: 11 clips
- Species (clip-level membership; `Zebra` is the coarse KABR label, see note below):
  - Zebra: 18 clips
  - Grevys Zebra: 17 clips
  - Giraffe: 15 clips
  - Plains Zebra: 6 clips
- Bbox size class (relative to this survey's range):
  - medium: 32 clips
  - far: 24 clips
  - close: 20 clips
- Habitat structural class (original free text in `habitat_notes`):
  - closed: 15 clips
  - mixed: 13 clips
  - open: 11 clips
  - unknown: 2 clips
- Clips with ground-truth pose: **26** (5 pose-annotated videos).

## Dataset Creation

### Curation Rationale

Prior drone-ecology evaluation is end-to-end and offline. This library isolates the *maneuvers* an
autonomous drone must perform (approaching, following a herd, responding to disturbance, capturing a
desired viewpoint) into short replayable clips with aligned ground truth, so navigation policies can
be benchmarked per maneuver under realistic perception conditions.

### Source Data

KABR raw video release (Mpala Research Centre drone surveys, January 2023; DJI aircraft, 20–50 m altitude).
Clips are cut from the original videos; labels are joined from the KABR per-frame occurrence records.
See [KABR-mini-scene-raw-videos](https://huggingface.co/datasets/imageomics/KABR-mini-scene-raw-videos) and 
[KABR-raw-videos](https://huggingface.co/datasets/imageomics/KABR-raw-videos)

### Annotations — label provenance (read this)

Labels come from three distinct sources:

1. **Bounding box, species, behaviour — KABR expert ground truth.** Frame-by-frame CVAT annotations
   from the KABR project; carried over verbatim (duplicate merge-rows collapsed to one row per track per frame).
2. **Pose (8-class viewpoint) — ground-truth cross-reference, sparse.** Sourced from the manually
   labeled `imageomics/KABR-poses` crops (5 videos: DJI_0002, DJI_0006, DJI_0070, DJI_0145, DJI_0208).
   We deliberately do **not** run the DINOv2 pose classifier here: it was trained on these same KABR
   crops, so applying it would be inference on training data. Because a crop's filename identifies its
   frame but not its track, each labeled crop is matched to a KABR track by **visual disambiguation**
   among the candidate boxes at that frame; matches below a similarity threshold are flagged
   `pose_provenance = "gt-ambiguous"`. Of 227 crop assignments, 125 were flagged
   ambiguous (see `catalog/pose_audit.csv`). Pose coverage is sparse by design.
3. **Individual ID — absent (future work).** No global re-identification ground truth exists; only the
   within-video `track_id` is provided. Cross-video re-ID (e.g. MegaDescriptor) is left for future work.

#### Who are the annotators?

KABR project annotators (bounding boxes, behavior); the `imageomics/KABR-poses` curator (pose).
Maneuver-suitability tags are assigned programmatically by the selection pipeline.

### Personal and Sensitive Information

Wildlife only; no personal data. Species include Grevy's zebra (endangered) — coordinates are at
survey-session granularity.

## Considerations for Using the Data

### Bias, Risks, and Limitations

- **Pose is sparse** (5 videos) and best-effort track-matched; trust the `pose_provenance`/`pose_match_score` columns.
- **Species skew** toward Grevy's zebra; single site (Mpala); small bbox sizes throughout (20–50 m altitude),
  so "close/medium/far" are relative to this survey, not absolute scale.
- Labels are KABR ground truth, not model-generated — appropriate for ground-truth evaluation, not for
  characterizing perception error.
- Species/behaviour strings are normalized (`Walking`→`Walk`, `Grevy`→`Grevys Zebra`). The generic
  **`Zebra`** label is retained from KABR and means **undifferentiated zebra — Plains and/or Grevy's**:
  on those sessions the annotators did not split the two subspecies, so a `Zebra` clip may contain
  Plains zebra, Grevy's zebra, or both. This is a labeling-granularity difference, not noise; the
  explicit `Plains Zebra` / `Grevys Zebra` labels are used only where KABR distinguished them.
- Because GT pose is sparse, the **SoI-aware** maneuver labels are mostly `hover` (no-pose) on this
  release; the maneuver is fully exercised only with dense (model-generated) pose.

### Recommendations

Filter on `pose_provenance == "gt"` for confident pose; use `suitable_maneuvers` to select clips per
maneuver; cross-reference `session_id` to the KABR/FAIR² metadata for habitat and herd context.

## Licensing Information

This dataset (the compilation) is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).
Please also cite the original KABR and KABR-poses datasets.

## Citation

**BibTeX:**

**Data**
```
@misc{drone_maneuver_clips,
  author = {Kline, Jenna and others},
  title = {Drone Maneuver Test-Clip Library},
  year = {2026},
  url = {https://huggingface.co/datasets/imageomics/drone-maneuver-clips},
  publisher = {Hugging Face}
}
```

Please also cite the original data sources:
```
@misc{KABR_Raw_Videos,
    author    = { Jenna Kline and Maksim Kholiavchenko and Michelle Ramirez and Samuel Stevens and Alec Sheets and Reshma Ramesh Babu and Namrata Banerji and Elizabeth Campolongo and Matthew Thompson and Nina Van Tiel and Jackson Miliko and Neil Rosser and Isla Duporge and Charles Stewart and Tanya Berger-Wolf and Daniel Rubenstein },
    title     = { KABR Raw Videos: Unprocessed Drone Footage for Kenyan Animal Behavior Analysis (Revision d002bb6) },
    year      = 2026,
    url       = { https://huggingface.co/datasets/imageomics/KABR-raw-videos },
    doi       = { 10.57967/hf/8170 },
    publisher = { Hugging Face }
}

@misc{kabr-mini-scene-videos,
    author    = { Jenna Kline and Maksim Kholiavchenko and Michelle Ramirez and Samuel Stevens and Alec Sheets and Reshma Ramesh Babu and Namrata Banerji and Elizabeth Campolongo and Matthew Thompson and Nina Van Tiel and Jackson Miliko and Isla Duporge and Neil Rosser and Eduardo Bessa and Charles Stewart and Tanya Berger-Wolf and Daniel Rubenstein },
    title     = { Kenyan Animal Behavior Recognition (KABR) Mini-Scene Raw Videos },
    year      = 2026,
    url       = { https://huggingface.co/datasets/imageomics/KABR-mini-scene-raw-videos },
    publisher = { Hugging Face }
}
```

## Acknowledgements

This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

## Dataset Card Authors

Generated by the `clip_library` pipeline; curated by Jenna Kline

## Dataset Card Contact

See the project repository.