从广播画面到小地图：实现最先进的SoccerNet比赛状态重建
From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction
AI总结：
本文提出一种基于单摄像头的端到端比赛状态重建流程，融合YOLOv5m检测、SegFormer相机参数估计与增强版DeepSORT跟踪，在SoccerNet 2024挑战赛中取得第一名。
摘要：
比赛状态重建（GSR）是体育视频理解中的一项关键任务，涉及在真实世界坐标中对足球场上所有个体——球员、守门员、裁判及其他人员——进行精确跟踪与定位。这一能力使教练和分析师能够从球员移动、球队阵型和比赛动态中获得可操作的见解，最终优化训练策略并增强竞争优势。使用单摄像头设置实现准确的GSR极具挑战性，原因在于频繁的相机运动、遮挡以及动态场景内容。在这项工作中，我们提出了一个稳健的端到端流程，用于在整场比赛中使用单摄像头设置跟踪球员。我们的解决方案集成了经过微调的YOLOv5m用于目标检测、基于SegFormer的相机参数估计器，以及基于DeepSORT的跟踪框架，并增强了重识别、朝向预测和球衣号码识别功能。通过同时保证空间准确性和时间一致性，我们的方法实现了最先进的比赛状态重建，在SoccerNet比赛状态重建挑战赛2024中夺得第一名，并显著优于其他竞争方法。
英文摘要：
Game State Reconstruction (GSR), a critical task in Sports Video Understanding, involves precise tracking and localization of all individuals on the football field-players, goalkeepers, referees, and others - in real-world coordinates. This capability enables coaches and analysts to derive actionable insights into player movements, team formations, and game dynamics, ultimately optimizing training strategies and enhancing competitive advantage. Achieving accurate GSR using a single-camera setup is highly challenging due to frequent camera movements, occlusions, and dynamic scene content. In this work, we present a robust end-to-end pipeline for tracking players across an entire match using a single-camera setup. Our solution integrates a fine-tuned YOLOv5m for object detection, a SegFormer-based camera parameter estimator, and a DeepSORT-based tracking framework enhanced with re-identification, orientation prediction, and jersey number recognition. By ensuring both spatial accuracy and temporal consistency, our method delivers state-of-the-art game state reconstruction, securing first place in the SoccerNet Game State Reconstruction Challenge 2024 and significantly outperforming competing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
