FootBots: A Transformer-based Architecture for Motion Prediction in Soccer

Autor:	Capellera, Guillem, Ferraz, Luis, Rubio, Antonio, Agudo, Antonio, Moreno-Noguer, Francesc
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Multiagent Systems
Druh dokumentu:	Working Paper
Popis:	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. https://youtu.be/9kaEkfzG3L8 Comment: Published as a conference paper at IEEE ICIP 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.19852 Zobrazit plný text záznamu View this record from Arxiv