运动分析中的玩家组活动识别转换器中的层次查询设计和分布式注意力
1College of Physical Education and Recreation, Guangdong Ocean University, Zhanjiang, 524000, China.
Scientific reports
|August 27, 2025
概括
本研究介绍了高级体育活动识别的层次注意力查询转换器 (HAQT). 通过精确识别群体活动而不依赖边界框来提高计算机视觉.
科学领域:
- 计算机视觉
- 人工智能
- 体育分析
背景情况:
- 由于玩家的互动和动态场景的复杂性,在体育运动中对小组活动的认知具有挑战性.
- 目前的方法通常依赖于准确的边界框检测,导致阻塞和特征纠的限制.
- 现有的方法很难有效地整合上下文信息进行可靠的分析.
研究的目的:
- 使用变压器架构开发体育运动员团体活动识别的新框架.
- 通过脱个人和团体活动的识别和改进特征解来解决现有方法的局限性.
- 通过先进的注意力机制, 增强对阻塞的强度,
主要方法:
- 引入了具有双路径架构的层次关注查询变压器 (HAQT).
- 采用层次查询设计,以有效地解开个人和团体级别的特征.
- 使用分布式注意力机制在玩家群体内和之间进行精细的沟通.
- 包含可变形的变压器骨干,用于多尺度的时空特征的动态聚合.
- 实现了一个集预测范式,以消除对边界框准确性的依赖.
主要成果:
- 在排球数据集上,HAQT实现了最先进的性能,平均精度 (mAP) 为92. 8%.
- 在篮球-51数据集上表现出卓越的性能,达到92.76%的准确性.
- 该模型有效地模拟了复杂的时空依赖性,并显示了阻塞和动态场景的稳定性.
结论:
- 拟议的HAQT模型显著提升了体育计算机视觉中的群体活动识别.
- 层次查询设计和分布式注意力框架为复杂的体育分析提供了强大的解决方案.
- HAQT的设置预测范式提供了精确的玩家定位和活动分类,优于现有的方法.
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