SoccerChat: Integrating Multimodal Data for Enhanced Soccer Game Understanding
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912387418816512 |
|---|---|
| author | Gautam, Sushant Midoglu, Cise Thambawita, Vajira Riegler, Michael A. Halvorsen, Pål Shah, Mubarak |
| author_facet | Gautam, Sushant Midoglu, Cise Thambawita, Vajira Riegler, Michael A. Halvorsen, Pål Shah, Mubarak |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SoccerChat: Integrating Multimodal Data for Enhanced Soccer Game Understanding Gautam, Sushant Midoglu, Cise Thambawita, Vajira Riegler, Michael A. Halvorsen, Pål Shah, Mubarak Computer Vision and Pattern Recognition Artificial Intelligence 68T45, 68T50 I.2.10; I.2.7; H.5.2 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 |
| title | SoccerChat: Integrating Multimodal Data for Enhanced Soccer Game Understanding |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T45, 68T50 I.2.10; I.2.7; H.5.2 |
| url | https://arxiv.org/abs/2505.16630 |