README.md

UniBallRacket Dataset

This directory contains the dataset for the UniBallRacket framework, covering three racket sports: badminton, table tennis, and tennis.

Directory Layout

data/
├── annotations/
│   └── dataset_info.json          # Global dataset metadata (clip list, splits)
│
├── info/                          # COCO-format annotations for RacketPose
│   ├── train_det_coco.json        # Detection: bbox annotations (train split)
│   ├── val_det_coco.json
│   ├── test_det_coco.json
│   ├── train_pose_coco.json       # Pose: keypoint annotations (train split)
│   ├── val_pose_coco.json
│   └── test_pose_coco.json
│
├── <sport>/                       # badminton / tabletennis / tennis
│   ├── videos/
│   │   └── <match>_<rally>.mp4    # Raw video clips
│   ├── all/
│   │   └── <match>/
│   │       ├── median.npz         # Median background frame (for BallTrack)
│   │       ├── frame/<rally>/     # Extracted JPG frames
│   │       ├── csv/<rally>_ball.csv        # Ball ground truth annotations
│   │       └── racket/<rally>/*.json       # Racket ground truth annotations
│   └── info/
│       ├── metainfo.json          # Sport-specific metadata
│       ├── train.json             # [[match_id, rally_id], ...] for training
│       ├── val.json               # Validation split
│       └── test.json              # Test split
│
└── data_traj/                     # Pre-built trajectory prediction datasets
    ├── ball_racket_<sport>_h20_f5.pkl    # Short-horizon: 20 history → 5 future
    └── ball_racket_<sport>_h80_f20.pkl   # Long-horizon: 80 history → 20 future

Data Formats

Ball Annotations (csv/<rally>_ball.csv)

Column	Type	Description
Frame	int	0-indexed frame number
X	int	Ball center X in pixels (1920×1080)
Y	int	Ball center Y in pixels
Visibility	int	1 = visible, 0 = not visible

Racket Annotations (racket/<rally>/<frame_id>.json)

Per-frame JSON with a list of racket instances, each containing:

{
  "category": "badminton_racket",
  "bbox_xywh": [x, y, w, h],
  "keypoints": [[x1, y1, vis], [x2, y2, vis], ...]
}

5 keypoints are defined: top, bottom, handle, left, right.

COCO Annotations (info/*_coco.json)

Standard COCO format used by RacketPose for training/evaluation:

• Detection (*_det_coco.json): 3 categories — badminton_racket, tabletennis_racket, tennis_racket.
• Pose (*_pose_coco.json): 1 category (racket) with 5 keypoints.

Trajectory PKL (data_traj/*.pkl)

Pickle files containing pre-processed sliding-window samples. Each PKL has:

{
    'train_samples': [...],   # List of sample dicts
    'test_samples': [...],
    'train_dataset': ...,     # Legacy Dataset objects
    'test_dataset': ...,
    'metadata': {
        'history_len': 80,
        'future_len': 20,
        'sports': ['badminton'],
        'total_samples': N,
        'train_size': ...,
        'test_size': ...
    }
}

Each sample dict:

{
    'history': np.array(shape=(H, 2)),       # Normalised [X, Y] in [0, 1]
    'future': np.array(shape=(F, 2)),
    'history_rkt': np.array(shape=(H, 10)),  # 5 keypoints × 2 coords, normalised
    'future_rkt': np.array(shape=(F, 10)),
    'sport': str,
    'match': str,
    'sequence': str,
    'start_frame': int
}

Normalisation: Ball coordinates are divided by (1920, 1080). Racket keypoints are divided by the same values.

Split Files (<sport>/info/train.json)

JSON list of [match_id, rally_id] pairs:

[["match1", "000"], ["match1", "001"], ...]

Generating Data from Scratch

If you have the raw videos, use DataPreprocess/ scripts to prepare all intermediate files:

cd DataPreprocess

# 1. Extract video frames to JPG
python extract_frames.py --data_root ../data --sport badminton

# 2. Compute median background frame
python create_median.py --data_root ../data --sport badminton

# 3. Generate dataset_info.json and per-sport split files
python generate_dataset_info.py --data_root ../data

# 4. Generate COCO annotations for RacketPose
python generate_coco_annotations.py --data_root ../data

Generating Trajectory Data

After running BallTrack and RacketPose inference, build data_traj/ PKLs:

cd TrajPred

# Interpolate short gaps in ball predictions
python linear_interpolate_ball_traj.py --data_root ../data --sport badminton

# Merge racket predictions with ground truth annotations
python merge_gt_with_predictions.py --data_root ../data --sport badminton

# Build PKL dataset
python build_dataset.py --data_root ../data --sport badminton --history 80 --future 20