DeGCN:用于基于骨架的动作识别的可变形图形卷积网络.
概括
本研究介绍了一个可变形图卷积网络 (DeGCN),用于基于骨架的动作识别. DeGCN以适应的方式捕获信息关节,通过学习灵活的时空连接来提高准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 基于骨架的动作识别利用人体图形拓学.
- 现有的图形卷积网络 (GCN) 使用不灵活的消息聚合,无法考虑类内变化和骨架序列中的冗余连接.
研究的目的:
- 提出一种新的可变形图卷积网络 (DeGCN),用于基于骨架的适应性和准确的动作识别.
- 解决当前GCN在处理多样化的动作样本和杂的骨架数据方面的局限性.
主要方法:
- 开发了可变形图卷积网络 (DeGCN),可以在空间和时间图上进行可变形采样.
- 为时间特征集成的连续隐藏空间,以捕捉固有的动作连续性.
- 设计了一种创新的多部门框架,用于关节和骨的模式,平衡准确性和模型大小.
主要成果:
- 在NTU RGB+D,NTU RGB+D 120和NW-UCLA数据集上实现了最先进的性能.
- 证明了DeGCN能够适应性地捕获信息关节并感知歧视性受体场的能力.
- 展示了多部门框架在增强整体效应和模型效率方面的有效性.
结论:
- 拟议的DeGCN方法显著推进了基于骨架的动作识别.
- 可变形图形卷曲提供了一种更适合的方法,用于从骨架数据分析复杂的人类行为.
- 多部门框架为行动识别任务提供了强大而高效的解决方案.
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