OpenSportsLab/soccernetpro-localization-gymnastics

OSL Gymnastics Localization (Action Spotting)

This repository provides a Gymnastics action spotting / localization dataset in an OpenSportsLab / SoccerNet-style format.

The dataset is organized by split (train/, valid/, test/) with video clips and corresponding localization annotations in JSON.

---

📌 Task

• Task type: action_spotting (a.k.a. temporal action localization / event spotting)
• Sport domain: Artistic Gymnastics (VT, UB, BB, FX)
• Annotation granularity: clip-relative timestamps in milliseconds (position_ms)
• Label format: single-label events (one label per event)

This dataset supports fine-grained temporal localization of gymnastics elements such as:

• Vault phases (VT_0, VT_1, VT_2, VT_3)
• Uneven Bars elements (UB_circles_start, UB_dismounts_end, etc.)
• Balance Beam skills (BB_turns_start, BB_flight_salto_end, etc.)
• Floor Exercise elements (FX_back_salto_start, FX_leap_jump_hop_end, etc.)

---

📁 Main branch structure

Current structure on main:

main/
├── annotations-localization-train.json
├── annotations-localization-valid.json
├── annotations-localization-test.json
├── train/
│   ├── <clip>.mp4
│   └── ...
├── valid/
│   ├── <clip>.mp4
│   └── ...
└── test/
    ├── <clip>.mp4
    └── ...

• The three folders train/, valid/, test/ contain short gymnastics video clips (.mp4).

• The three JSON files contain the localization labels for the corresponding split.

• Clip filenames follow the pattern:

  <YouTubeID>_E_<start>_<end>.mp4
  

  Example:

  0LtLS9wROrk_E_000147_000152.mp4
  

---

🧾 Annotation format

Each annotation file follows a SoccerNet-like schema.

Top-level keys:

• version: format version (e.g., "2.0")
• task: "action_spotting"
• dataset_name: dataset identifier
• labels: list of valid event classes under a given head_name
• data: list of items (each item corresponds to one clip)

---

data[] item fields

Each item contains:

• id: stable item identifier
• inputs: list containing a video descriptor
• events: list of labeled events in that clip
• metadata: extra information such as fps, width, height, segment boundaries, etc.

Example (simplified):

{
  "id": "Gymnastics_0LtLS9wROrk_E_000147_000152",
  "inputs": [
    {
      "type": "video",
      "path": "test/0LtLS9wROrk_E_000147_000152.mp4",
      "fps": 29.97
    }
  ],
  "events": [
    {
      "head": "gymnastics_action",
      "label": "VT_0",
      "position_ms": "1201",
      "comment": "round-off, flic-flac on, stretched salto backward with 1 turn off"
    }
  ]
}

---

⏱️ Timestamp ↔ video position relationship (IMPORTANT)

For each event:

• position_ms is clip-relative time in milliseconds.
• It is computed from the clip-relative frame index using:

position_ms = round(frame / fps * 1000)

So:

• position_ms = 0 corresponds to the first frame of the clip.
• position_ms = 4240 means the event happens around 4.240 seconds after the clip start.

If you need the approximate frame index back:

frame ≈ round(position_ms / 1000 * fps)

---

🏷️ Labels

Labels are stored under:

labels.<head_name>.labels

where <head_name> is typically:

gymnastics_action

Example classes:

• BB_turns_start
• BB_turns_end
• FX_back_salto_start
• FX_back_salto_end
• UB_circles_start
• UB_circles_end
• UB_transition_flight_start
• UB_dismounts_end
• VT_0
• VT_1
• VT_2
• VT_3

Each event is single-label, meaning one action per timestamp.

---

🧩 Clip naming convention

Clips are derived from longer competition videos.

Pattern:

<YouTubeID>_E_<start>_<end>

Where:

• <YouTubeID>: original competition video ID
• _E: event version marker
• <start> / <end>: start and end time in seconds within the original long video

Example:

0LtLS9wROrk_E_000147_000152

This means:

• Original video: 0LtLS9wROrk

• Clip covers seconds 147 to 152

• The clip file is stored as:

  test/0LtLS9wROrk_E_000147_000152.mp4
  

---

🧰 Notes

• Paths in inputs[].path are relative paths pointing to the split folder:

  • train/<clip>.mp4
  • valid/<clip>.mp4
  • test/<clip>.mp4

• position_ms values are clip-relative, not global video timestamps.

• Clips may contain padding before or after annotated actions.

• The repository includes .gitattributes for Git/LFS handling of large video files.

---

✅ Quick sanity check

Pick one entry in annotations-localization-test.json:

1. Open the clip video located at:

   test/<clip>.mp4
   

2. Convert position_ms to seconds:

   seconds = position_ms / 1000
   

3. Jump to that time in the video.

4. You should observe the corresponding gymnastics element near that timestamp.

---

📚 Data Source & Attribution

The gymnastics clips and raw annotations in this dataset are derived from the tennis data released in the official repository of the paper:

Spotting Temporally Precise, Fine-Grained Events in Video (ECCV 2022)
James Hong, Haotian Zhang, Michaël Gharbi, Matthew Fisher, Kayvon Fatahalian

Source repository (gymnastics data):
hhttps://github.com/jhong93/spot/tree/main/data/finegym

If you use this dataset, please cite the original paper:

@inproceedings{precisespotting_eccv22,
    author={Hong, James and Zhang, Haotian and Gharbi, Micha\"{e}l and Fisher, Matthew and Fatahalian, Kayvon},
    title={Spotting Temporally Precise, Fine-Grained Events in Video},
    booktitle={ECCV},
    year={2022}
}