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pretty_name: RoboTrack Real v3
tags:
- video
- robotics
- computer-vision
- point-tracking
configs:
- config_name: default
data_files:
- split: train
path:
- train/metadata.parquet
- train/*/video.mp4
- train/*/point_track_vis.mp4
drop_labels: true
---
# RoboTrack Real v3
RoboTrack Real v3 is an evaluation dataset of 318 real-world video clips with
sparse 2D point trajectories and visibility annotations. Each example includes
the original RGB video, a rendered visualization of its point tracks, and the
underlying NumPy annotation archive.
## Dataset Viewer
The viewer exposes two playable video columns:
- `raw`: the original RGB clip (`raw_file_name` in `metadata.parquet`)
- `visualization`: the RGB clip with colored, numbered tracks and one second of
visibility-aware trajectory history (`visualization_file_name`)
The remaining columns provide clip dimensions, timing, annotation statistics,
and optional `review_status` and `review_notes` fields for annotator review.
## Layout
```text
train/
metadata.parquet
<clip_id>/
video.mp4
point_track_vis.mp4
point_tracks.npz
scripts/
visualize_robotrack_dataset.py
```
All media files are stored directly in this repository; there are no symbolic
links. Videos are H.264 with matching frame counts and timing between the raw
and visualization versions.
## Annotation format
Each `point_tracks.npz` contains:
- `trajs_2d`: `float32` array shaped `(T, N, 2)` containing pixel coordinates
in `(x, y)` order
- `visibility`: `float32` array shaped `(T, N)`, where values greater than `0.5`
are visible
- `query_frames`: `int32` array shaped `(N,)` containing the query frame for
each track
Here, `T` is the number of video frames and `N` is the number of annotated
tracks. Invisible coordinates are stored as `(0, 0)`.
## Frame rates
- 174 clips at 15 FPS
- 1 clip at 20 FPS
- 143 clips at 30 FPS
Forty-five clips whose containers incorrectly reported 60 FPS were retimed to
15 FPS without dropping frames. Their H.264 streams were copied without lossy
re-encoding, and the visualization videos use the same corrected timing.
## Loading
```python
from datasets import load_dataset
dataset = load_dataset("<namespace>/robotrack-real-v3", split="train")
example = dataset[0]
print(example["clip_id"], example["num_tracks"])
```
The NPZ path for each example is available in `annotation_path`. The included
renderer can recreate the point-track videos if needed.
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