research-article

P²ANet: A Large-Scale Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos

Authors:
Jiang Bian

Baidu Inc., China

Baidu Inc., China

0000-0001-6997-1989
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,
Xuhong Li

Baidu Inc., China

Baidu Inc., China

0000-0002-2582-8256
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,
Tao Wang

Baidu Inc., China

Baidu Inc., China

0000-0003-4740-6932
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,
Qingzhong Wang

Baidu Inc., China

Baidu Inc., China

0000-0003-1562-8098
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,
Jun Huang

Baidu Inc., China

Baidu Inc., China

0009-0003-2696-6452
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,
Chen Liu

Baidu Inc., China

Baidu Inc., China

0009-0007-1671-7427
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,
Jun Zhao

Baidu Inc., China

Baidu Inc., China

0009-0004-0522-8079
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,
Feixiang Lu

Baidu Inc., China

Baidu Inc., China

0000-0003-3952-4402
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,
Dejing Dou

Baidu Inc., China

Baidu Inc., China

0000-0001-7561-1672
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,
Haoyi Xiong

Baidu Inc., China

Baidu Inc., China

0000-0002-5451-3253
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ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20 Issue 4Article No.: 118pp 1–23https://doi.org/10.1145/3633516

Published:11 January 2024Publication History

ACM Transactions on Multimedia Computing, Communications, and Applications

Abstract

While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging. In this work, we release yet another sports video benchmark P²ANet for Ping Pong-Action detection, which consists of 2,721 video clips collected from the broadcasting videos of professional table tennis matches in World Table Tennis Championships and Olympiads. We work with a crew of table tennis professionals and referees on a specially designed annotation toolbox to obtain fine-grained action labels (in 14 classes) for every ping-pong action that appeared in the dataset, and formulate two sets of action detection problems—action localization and action recognition. We evaluate a number of commonly seen action recognition (e.g., TSM, TSN, Video SwinTransformer, and Slowfast) and action localization models (e.g., BSN, BSN++, BMN, TCANet), using P²ANet for both problems, under various settings. These models can only achieve 48% area under the AR-AN curve for localization and 82% top-one accuracy for recognition since the ping-pong actions are dense with fast-moving subjects but broadcasting videos are with only 25 FPS. The results confirm that P²ANet is still a challenging task and can be used as a special benchmark for dense action detection from videos. We invite readers to examine our dataset by visiting the following link: https://github.com/Fred1991/P2ANET.

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Index Terms

P²ANet: A Large-Scale Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published in
ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 4
April 2024
676 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3613617
Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
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Publication History
- Published: 11 January 2024
- Online AM: 28 November 2023
- Accepted: 21 October 2023
- Revised: 20 September 2023
- Received: 27 March 2023
Published in tomm Volume 20, Issue 4

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Datasets
annotation toolbox
video analysis
action recognition and localization
table tennis
Qualifiers
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P²ANet: A Large-Scale Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos

ACM Transactions on Multimedia Computing, Communications, and Applications

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REFERENCES

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