Action Anticipation from SoccerNet Football Video Broadcasts

• URL: http://arxiv.org/abs/2504.12021v1
• Date: Wed, 16 Apr 2025 12:24:33 GMT
• Title: Action Anticipation from SoccerNet Football Video Broadcasts
• Authors: Mohamad Dalal, Artur Xarles, Anthony Cioppa, Silvio Giancola, Marc Van Droogenbroeck, Bernard Ghanem, Albert Clapés, Sergio Escalera, Thomas B. Moeslund,
• Abstract summary: We introduce the task of action anticipation for football broadcast videos.<n>We predict future actions in unobserved future frames within a five- or ten-second anticipation window.<n>Our work will enable applications in automated broadcasting, tactical analysis, and player decision-making.
• Score: 84.87912817065506
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artificial intelligence has revolutionized the way we analyze sports videos, whether to understand the actions of games in long untrimmed videos or to anticipate the player's motion in future frames. Despite these efforts, little attention has been given to anticipating game actions before they occur. In this work, we introduce the task of action anticipation for football broadcast videos, which consists in predicting future actions in unobserved future frames, within a five- or ten-second anticipation window. To benchmark this task, we release a new dataset, namely the SoccerNet Ball Action Anticipation dataset, based on SoccerNet Ball Action Spotting. Additionally, we propose a Football Action ANticipation TRAnsformer (FAANTRA), a baseline method that adapts FUTR, a state-of-the-art action anticipation model, to predict ball-related actions. To evaluate action anticipation, we introduce new metrics, including mAP@$\delta$, which evaluates the temporal precision of predicted future actions, as well as mAP@$\infty$, which evaluates their occurrence within the anticipation window. We also conduct extensive ablation studies to examine the impact of various task settings, input configurations, and model architectures. Experimental results highlight both the feasibility and challenges of action anticipation in football videos, providing valuable insights into the design of predictive models for sports analytics. By forecasting actions before they unfold, our work will enable applications in automated broadcasting, tactical analysis, and player decision-making. Our dataset and code are publicly available at https://github.com/MohamadDalal/FAANTRA.

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