基于双GCN的棒球行为识别模型的分析,改进了运动重量
1School of Sports Training, Jilin Sport University, Changchun, 130022, China. 0274@jlsu.edu.cn.
Scientific reports
|July 15, 2025
概括
这项研究引入了一种新的双图卷积网络模型,以提高棒球行为识别精度. 改进后的模型显著提高了性能,在识别棒球动作方面达到高达94.84%的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 运动分析 运动分析
- 机器学习 机器学习
背景情况:
- 棒球行为识别在准确性和角色互动方面面临着挑战.
- 现有的模型很难有效地捕捉棒球行动的细微差别.
研究的目的:
- 开发一个改进的模型,准确地识别棒球行为.
- 为了增强棒球视频中角色之间的联系和贡献.
- 为了克服当前棒球动作识别系统的局限性.
主要方法:
- 提出了一个基于双图卷积网络 (DGCN) 的新型运动重量改善模型.
- 在棒球视频图像中,DGCN被用于行为识别和关键区域细分.
- 动作权重被纳入,以增强角色的相关性和贡献.
主要成果:
- 该模型在1/2w的速率,1/2H的关键区域宽度和2个关键区域实现了最佳性能.
- 最高的精度达到了94.84%,超过了层次时间深度模型的12.06%.
- 整合运动权重进一步提高了3.45%的准确性.
结论:
- 拟议的双图卷积网络模型显著提高了棒球行为识别准确度.
- 运动重量改进有效地提高了性能和角色互动分析.
- 这项研究为推进体育分析,特别是棒球的行为识别提供了有价值的见解.
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