通过改进的YOLOv8检测和基于DBSCAN的团队分类来自动估计足球占有率
Rong Guo1,2,3, Yucheng Zeng1,2, Rong Deng1
1College of Big Data, Yunnan Agricultural University, Kunming 650201, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了一种深度学习框架,用于使用计算机视觉进行自动足球占有追踪. 这种新系统提高了体育分析的准确性和效率,优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 运动分析 运动分析
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 传统的体育分析依赖于手动数据,这是耗时和主观的.
- 足球分析的自动化需要准确和客观的方法来跟踪比赛事件.
研究的目的:
- 开发一个深度学习框架,从广播视频中精确估计足球占有率.
- 消除在体育分析中需要手动注释和基于事件的数据的需求.
主要方法:
- 使用YOLOv8-P2S3A和YOLOv8-HWD3A进行对象检测 (足球和球员).
- 采用DBSCAN集群用于基于球衣颜色的无监督团队识别.
- 集成的Norfair多对象跟踪和时间精细化模块,以获得持有时间的准确性.
主要成果:
- 取得了足球 (79.4%) 和球员检测 (71.1%) 的高验证平均精度.
- 该系统表现出优异的占有估计,根平均平方误差 (RMSE) 为4.87.
- 超过了基线模型的表现,例如YOLOv10n (RMSE:5.12) 和YOLOv11 (RMSE:5.17).
结论:
- 拟议的框架大大提高了足球分析的精度,效率和自动化.
- 为教练,分析师和体育科学家在专业环境中提供实用价值.
- 证明了深度学习对客观体育数据分析的有效性.
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