README.md

metadata

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

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

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.