轻量级图形卷积网络具有多重注意力机制,用于在线体育教育中的智能动作识别
1Department of Physical Education, Tongji University, Shanghai, China.
PeerJ. Computer science
|September 24, 2025
概括
一个新的轻量级图形卷积网络 (GCN) 从3D骨架数据中有效地识别复杂的人类运动. 该模型为在线体育和运动分析应用提供了高精度.
科学领域:
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
- 运动科学 运动科学 运动科学
背景情况:
- 由于现有的动作识别模型的局限性,在线体育教育在识别复杂的运动和提供个性化的反方面面临挑战.
- 目前的模型是计算密集型的,并且缺乏跨多样化的骨架模式的概括性,阻碍了它们在实时场景中的应用.
研究的目的:
- 利用3D骨架数据开发一个计算效率高,准确的人类行动识别模型.
- 解决现有模型在计算成本和通用性方面的局限性,用于在线体育教育等应用.
主要方法:
- 提出了一个轻量级的图形卷积网络 (GCN),集成了一个改进的幽灵模块与全球注意力机制 (GAM) 和通道注意力机制 (CAM).
- 该模型增强了对3D骨架序列的空间和时间特征提取,优化了实时效率.
- 对NTU60RGB+D数据集的估计计算成本 (每推断6.2GFLOP) 和性能.
主要成果:
- 在NTU60RGB+D数据集上,在跨主题设置中获得了90.8%的准确性,在跨视图设置中获得了96.8%的准确性.
- 与基线ST-GCN相比,拟议的GCN模型需要60%以上的计算 (6.2 GFLOPs) 较少.
- 证明了高精度和计算效率之间的平衡.
结论:
- 开发的轻量级GCN模型有效地识别了高精度和效率的人类行为.
- 这个模型显示了在线体育教育,康复监测,老年人的运动分析和虚拟现实接口的增强的重大前景.
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