P2ANET

Dataset Overview

Dataset Name: P2ANET

Source: This dataset originates from the paper "P2ANet: A Large‑Scale Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos" by Bian, Jiang et al., published in 2023 in ACM Transactions on Multimedia Computing, Communications and Applications.

Data Collection Method

Video Capture: Recorded using an RGB monocular camera.

Label Acquisition: Performed through manual annotation.

Data Description

Organization:

• Raw Dataset: Divided into v1 and v2 subsets, each containing video files and corresponding label files.
• Processed Dataset: Consolidates extracted frames and labels from both v1 and v2.

File Structure:

P2A_dataset/ -dataset/ -video/ -v1/ -0000000.mp4 -0000001.mp4 -… -v2/ -0000000.mp4 -0000001.mp4 -… -label/ -v1.json -v2.json -proj.json # Mapping between original video names and ID names

Dataset Access

Online Repository: Baidu Wangpan

Access Key: Request via email (bj911125@outlook.com).

Citation

@article{bian2023p2anet, title={P2ANet: A Large‑Scale Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos}, author={Bian, Jiang and Li, Xuhong and Wang, Tao and Wang, Qingzhong and Huang, Jun and Liu, Chen and Zhao, Jun and Lu, Feixiang and Dou, Dejing and Xiong, Haoyi}, journal={ACM Transactions on Multimedia Computing, Communications and Applications}, year={2023}, publisher={ACM New York, NY} }

License

License Type: MIT License