P2ANET
The P2ANET dataset is a large‑scale benchmark for dense action detection from table‑tennis broadcast videos. It consists of two parts: a raw dataset and a processed dataset, collected in two batches (v1 and v2). Video data were captured with an RGB monocular camera, and labels were obtained via manual annotation.
Dataset description and usage context
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
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