Publication
FootBots: A transformer-based architecture for motion prediction in soccer
Conference Article
Conference
IEEE International Conference on Image Processing (ICIP)
Edition
2024
Pages
2313-2319
Doc link
http://dx.doi.org/10.1109/ICIP51287.2024.10647396
File
Abstract
Motion prediction in soccer involves capturing complex dynamics from player and ball interactions. We present FootBots, an encoder-decoder transformer-based architecture addressing motion prediction and conditioned motion prediction through equivariance properties. FootBots captures temporal and social dynamics using set attention blocks and multi-attention block decoder. Our evaluation utilizes two datasets: a real soccer dataset and a tailored synthetic one. Insights from the synthetic dataset highlight the effectiveness of FootBots' social attention mechanism and the significance of conditioned motion prediction. Empirical results on real soccer data demonstrate that FootBots outperforms baselines in motion prediction and excels in conditioned tasks, such as predicting the players based on the ball position, predicting the offensive (defensive) team based on the ball and the defensive (offensive) team, and predicting the ball position based on all players. Our evaluation connects quantitative and qualitative findings.
Categories
computer vision.
Author keywords
Motion prediction, Signal forecasting, Transformer, Trajectory understanding, Soccer.
Scientific reference
G. Capellera, L. Ferraz, A. Rubio, A. Agudo and F. Moreno-Noguer. FootBots: A transformer-based architecture for motion prediction in soccer, 2024 IEEE International Conference on Image Processing, 2024, Abu Dhabi, UAE, pp. 2313-2319.
Follow us!