Existing general sports datasets (e.g., Kinetic 400) contain limited table tennis footage primarily designed for action classification. Other specific datasets, such as Baidu's P2Anet, label service actions but lack critical data on ball spin and landing point.

To address these limitations and support the development of AI systems that learn progressively—from coaching demonstrations to amateur play, and finally to professional matches—we constructed the BNDSTT (Beijing National Day School Table Tennis) dataset.

2. Data Composition

The dataset currently consists of 997 table tennis service videos:

20% (200 videos): High-quality instructional service videos curated from Bilibili.
80% (797 videos): Real-world training and match footage featuring the author and other school team members.

3. Preprocessing & Construction

To ensure model focus and accuracy, the data underwent specific preprocessing:

Subject Extraction (YOLO)

We utilized a YOLO model to detect individuals in the frame. Using a "center-focused and largest-area" principle, the serving player was identified and cropped to minimize background noise.

Figure 1: YOLO cropping centered on the serving player.

Trajectory Truncation

For the landing point prediction task, videos were truncated to the moment the ball crosses the net. The goal is to predict the final landing spot based solely on the trajectory before the net.

Figure 2: Video truncation at the net-crossing moment.

📊 Dataset Statistics

Spin Distribution (Task 1)

The dataset covers 9 distinct spin types:

Spin Type	Count
Backspin	181
Topspin	84
Left Sidespin	57
Right Sidespin	34
Left-Side Topspin	130
Left-Side Backspin	134
Right-Side Topspin	168
Right-Side Backspin	204
No Spin	5

Landing Point Distribution (Task 2)

Landing positions are categorized into 6 zones:

Landing Position	Count
Middle Short	274
Left Long	274
Middle Long	246
Left Short	97
Right Long	57
Right Short	49

📝 Annotation Format

The annotation file follows a simple tabular structure:

<Filename> <Spin Code> <Landing Code>

Example: TableTennis_00015_RightSideTop_LeftShort.mp4 6 100

Label Encoding Reference

Spin Codes (0-8):

0: Topspin
1: Backspin
2: Left Sidespin
3: Right Sidespin
4: Left-Side Topspin
5: Left-Side Backspin
6: Right-Side Topspin
7: Right-Side Backspin
8: No Spin

Landing Codes (100-105):

100: Left Short
101: Right Short
102: Left Long
103: Right Long
104: Middle Short
105: Middle Long

📜 License & Ethical Usage

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC-4.0) license.

🚫 Non-Commercial Use Only

By downloading or accessing this dataset, you agree that:

The data will be used solely for academic research purposes.
Any commercial use, including but not limited to training commercial AI models or use in for-profit products, is strictly prohibited.

🔒 Ethical & Privacy Notice

This dataset contains video data recorded in a controlled sports environment and was collaboratively constructed by a team of high school researchers.

Privacy & Consent: To strictly respect the privacy preferences of all participants, data from students who did not wish to appear publicly has been excluded. This repository solely contains data from contributors who provided explicit consent for public release.
Facial Recognition Ban: Users must agree not to attempt to identify individuals in the videos or use the data for facial recognition tasks.

🖊️ Citation

If you use this dataset in your research, please cite it as follows:

@misc{li2025tabletennis,
  author = {Li, Zhanran},
  title = {BNDSTT: Table Tennis Serve Trajectory Dataset},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/J0LE/BNDSTT}},
  note = {Licensed under CC-BY-NC-4.0}
}

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🏓 BNDSTT Dataset Description

1. Dataset Overview & Motivation