单摄像头实现足球场上球员精确定位，精度领先
From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction
- 融合检测、相机参数估计与多目标跟踪的端到端方案
- 在SoccerNet挑战赛中排名第一，显著超越现有方法
- 适合体育分析、智能教练系统等场景使用
游戏状态重建（GSR）是体育视频理解中的关键任务，需在真实坐标系下精确追踪足球场上所有人员——球员、守门员、裁判等。该能力使教练和分析师能获取球员移动、阵型变化和比赛动态的可操作洞察，优化训练策略并提升竞技优势。由于摄像机频繁运动、遮挡及场景动态变化，仅用单摄像机实现高精度GSR极具挑战。本文提出一种鲁棒的端到端流水线，可基于单摄像机完成整场比赛的球员追踪。方案整合了微调后的YOLOv5m用于目标检测，基于SegFormer的相机参数估计算法，以及增强版DeepSORT跟踪框架（含重识别、朝向预测和球衣号码识别）。通过保障空间精度与时间一致性，本方法在SoccerNet Game State Reconstruction Challenge 2024中取得第一名，显著优于其他竞争方法。
原文摘要 · Abstract (English)
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.
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