在足球视频中实时检测小物体的高效变压器框架
1SCHOOL OF PHYSICAL EDUCATION, SHANDONG UNIVERSITY, Jinan, 250061, China. sdtyzwt@mail.sdu.edu.cn.
Scientific reports
|August 22, 2025
概括
这项研究介绍了足球转换器 (FoT),这是一种用于增强足球赛事分析的新型计算机视觉框架. 在足球视频中提高小物体和复杂场景的实时检测精度.
科学领域:
- 计算机视觉
- 人工智能
- 体育分析
背景情况:
- 传统的物体检测方法在复杂的足球视频场景如多视图,运动模糊和小物体识别中难以实现实时性能和准确性.
- 基于变压器的方法面临着高计算成本和细粒度目标信息的缓慢融合的挑战.
- 现有的方法缺乏全球语义理解和地方细节捕获之间的有效平衡.
研究的目的:
- 提出一个新的端到端检测框架,足球转换器 (FoT),以改进足球视频分析.
- 在对象检测中实现全球语义表达和本地细节捕获之间的有效平衡.
- 在复杂的足球场景中提高小物体的检测和整体准确性.
主要方法:
- 引入了局部交互聚合单元 (LIAU),通过特征聚合和窗口偏移,将自我注意的复杂性从O ((N^2) 降低到O ((N)).
- 开发了多级特征交互模块 (MFIM),用于对齐和融合多级特征,增强低级细节和高级语义集成.
- 设计的 FoT 是一个集成 LIAU 和 MFIM 的全新端到端检测框架.
主要成果:
- 与基线方法相比,FoT在Soccer-Det数据集上获得了3.0%的mAP改进,在FIFA-Vid数据集上获得了1.3%的收益.
- 该框架展示了实时推断速度,在复杂的足球视频条件下保持性能.
- 在小物体检测性能上观察到显著的改善.
结论:
- 拟议的足球转换器 (FoT) 框架有效地平衡全球和本地特征提取,以加强足球视频中的对象检测.
- 在体育分析中提供强大而高效的战术评估和事件分析解决方案.
- 这种方法在体育视频处理中具有很强的应用潜力.
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