一个可适应的多关联聚合网络,用于基于骨架的运动识别
Xinpeng Yin1, Jianqi Zhong1, Deliang Lian1
1Guangdong Multimedia Information Service Engineering Technology Research Center, Shenzhen University, Yuehai Street, Shenzhen, 518060, China.
Scientific reports
|November 6, 2023
概括
本研究介绍了基于3D骨架的动作识别的自适应多相关聚合网络 (AMANet). AMANet有效地模拟动态关节依赖,通过捕捉人类姿势中的非连接关系来提高识别准确性.
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:
- 图形卷积网络 (GCNs) 在基于3D骨架的运动识别方面表现有前途.
- 现有的方法往往忽略了人类关节和非连接的骨关系之间的动态相关性.
研究的目的:
- 提出一个新的网络,即自适应多相关聚合网络 (AMANet),用于增强基于3D骨架的运动识别.
- 为了有效地建模动态关节依赖性,并从非连接的骨结构中获取信息.
主要方法:
- 推出了AMANet的三个关键模块:空间特征提取模块 (SFEM),时间特征提取模块 (TFEM) 和空间时间特征提取模块 (STFEM).
- 从微分几何运动框架中利用相对关节坐标.
- 开发了一个数据预处理模块 (DP),以丰富骨架数据特征.
主要成果:
- 在三个公共数据集上证明了AMANet的有效性:NTU-RGB+D 60,NTU-RGB+D 120和Kinetics-Skeleton 400.
- 拟议的方法成功地捕获了动态关节依赖关系,这对于准确的运动识别至关重要.
结论:
- 通过解决现有的基于GCN的方法的局限性,AMANet在基于3D骨架的运动识别方面取得了重大进展.
- 该方法能够建模动态相关性和非连接的骨架信息,从而提高了性能.
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