Paper Reading AI Learner

SoccerChat: Integrating Multimodal Data for Enhanced Soccer Game Understanding

2025-05-22 13:01:51
Sushant Gautam, Cise Midoglu, Vajira Thambawita, Michael A. Riegler, P{\aa}l Halvorsen, Mubarak Shah

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. this https URL

Abstract (translated)

将人工智能技术融入体育数据分析,特别是在足球视频理解方面,已经改变了对复杂比赛动态的实时、自动化洞察。传统的方法依赖于孤立的数据流,这限制了它们捕捉比赛全貌的有效性。为了解决这个问题,我们推出了SoccerChat,这是一个多模态对话AI框架,它整合了视觉和文本数据以增强足球视频的理解能力。 通过利用丰富的SoccerNet数据集,并结合球衣颜色注释以及自动语音识别(ASR)转录内容,SoccerChat在经过结构化的视频指令数据集上进行微调,从而能够实现准确的比赛理解、事件分类和裁判决策。我们在动作分类和裁判决策任务上对SoccerChat进行了基准测试,展示了其在通用足球赛事理解方面的性能,并且在裁判决策方面保持了竞争性的准确性。 我们的研究结果强调了多模态整合在推进足球分析领域的关键作用,为更加互动和解释性强的AI驱动体育分析铺平道路。[参考链接](https://this-url)

URL

https://arxiv.org/abs/2505.16630

PDF

https://arxiv.org/pdf/2505.16630.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Time_Series Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot