SoccerChat: Integrating Multimodal Data for Enhanced Soccer Game Understanding

TL;DR

SoccerChat is a multimodal AI framework that combines visual and textual data to improve soccer game understanding, event classification, and referee decision analysis, advancing sports analytics with interactive and explainable AI.

Contribution

We introduce SoccerChat, a novel multimodal conversational AI that integrates visual and textual data for comprehensive soccer video analysis, leveraging the SoccerNet dataset and fine-tuning on structured instructions.

Findings

Effective in classifying soccer events

Maintains competitive accuracy in referee decision tasks

Highlights the importance of multimodal data integration

Abstract

The integration of artificial intelligence in sports analytics has transformed soccer video understanding, enabling real-time, automated insights into complex game dynamics. Traditional approaches rely on isolated data streams, limiting their effectiveness in capturing the full context of a match. To address this, we introduce SoccerChat, a multimodal conversational AI framework that integrates visual and textual data for enhanced soccer video comprehension. Leveraging the extensive SoccerNet dataset, enriched with jersey color annotations and automatic speech recognition (ASR) transcripts, SoccerChat is fine-tuned on a structured video instruction dataset to facilitate accurate game understanding, event classification, and referee decision making. We benchmark SoccerChat on action classification and referee decision-making tasks, demonstrating its performance in general soccer event comprehension while maintaining competitive accuracy in referee decision making. Our findings highlight the importance of multimodal integration in advancing soccer analytics, paving the way for more interactive and explainable AI-driven sports analysis. https://github.com/simula/SoccerChat