action anticipation; dataset; football; soccer; soccernet; sports; transformer; Action anticipations; Baseline methods; Broadcast video; Dataset; Football actions; Sport video; Transformer; Video broadcasts; Computer Vision and Pattern Recognition; Electrical and Electronic Engineering; SoccerNet
Abstract :
[en] 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@d, which evaluates the temporal precision of predicted future actions, as well as mAP@8, 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 decisionmaking. Our dataset and code are publicly available at https://github.com/MohamadDalal/FAANTRA.
Research Center/Unit :
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège TELIM VIULab
Disciplines :
Electrical & electronics engineering
Author, co-author :
Dalal, Mohamad; Aalborg University, Denmark
Xarles, Artur; Universitat de Barcelona, Spain ; Computer Vision Center, Spain
Cioppa, Anthony ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Giancola, Silvio; KAUST, Hong Kong
Van Droogenbroeck, Marc ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Télécommunications
Ghanem, Bernard; KAUST, Hong Kong
Clapes, Albert; Universitat de Barcelona, Spain ; Computer Vision Center, Spain
Escalera, Sergio; Aalborg University, Denmark ; Universitat de Barcelona, Spain ; Computer Vision Center, Spain
Moeslund, Thomas B.; Aalborg University, Denmark
Language :
English
Title :
Action Anticipation from Soccernet Football Video Broadcasts
Publication date :
June 2025
Event name :
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
This work has been partially supported by the Spanish project PID2022-136436NB-I00 and by ICREA under the ICREA Academia programme. This work is supported by the KAUST Center of Excellence for Generative AI under award number 5940.
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