TAL4Tennis: Temporal Action Localization in Tennis Videos Using State Space Models

Ahmed Jouini, Mohamed Ali Lajnef, Faten Chaieb-Chakchouk, Alex Loth

2025

Abstract

Temporal action localization is a classic computer vision problem in video understanding with a wide range of applications. In the context of sports videos, it is integrated into most of the current solutions used by coaches, broadcasters and game specialists to assist in performance analysis, strategy development, and enhancing the viewing experience. This work presents an application study on temporal action localization for tennis broadcast videos. We study and evaluate a foundational video understanding model for identifying tennis actions in match footage. We explore its architecture, specifically the state space model, from video input to the prediction of temporal segments and classification labels. Our experiments provide findings and interpretations of the model’s performance on tennis data. We achieved an average mean Average Precision (mAP) of 66.14% over all thresholds on the TenniSet dataset, surpassing the other methods, and 96.16% on our private French Open dataset.

Paper Citation

in Harvard Style

Jouini A., Lajnef M., Chaieb-Chakchouk F. and Loth A. (2025). TAL4Tennis: Temporal Action Localization in Tennis Videos Using State Space Models. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 398-406. DOI: 10.5220/0013168900003890

in Bibtex Style

@conference{icaart25,
author={Ahmed Jouini and Mohamed Lajnef and Faten Chaieb-Chakchouk and Alex Loth},
title={TAL4Tennis: Temporal Action Localization in Tennis Videos Using State Space Models},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={398-406},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013168900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - TAL4Tennis: Temporal Action Localization in Tennis Videos Using State Space Models
SN - 978-989-758-737-5
AU - Jouini A.
AU - Lajnef M.
AU - Chaieb-Chakchouk F.
AU - Loth A.
PY - 2025
SP - 398
EP - 406
DO - 10.5220/0013168900003890
PB - SciTePress