SPD-Net:具有动态图形网络的语义分区变压器,用于改进基于骨架的步态识别
Priyanka D1, Mala T1
1Department of Information Science and Technology, College of Engineering Guindy, Anna University, Chennai, 600 025, Tamil Nadu, India.
概括
本研究介绍了SPD-Net,一种使用动态图和变压器的新型步态识别方法,以提高准确性和减少计算负载. 通过有效分析人类步行模式,SPD-Net提高了生物识别安全性.
科学领域:
- 生物识别信息 生物识别信息
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 步态识别是一个关键的生物识别模式,但现有的基于轮的方法与变化作斗争.
- 基于模型的方法使用了骨架数据,但往往错过了语义关节关系.
- 变压器模型捕捉了远程依赖性,但在计算上是昂贵的.
研究的目的:
- 开发一个强大且计算效率高的步态识别系统.
- 通过建模复杂的关节关系来增强步态特征的表现.
- 克服现有的基于轮和基于模型的步态识别技术的局限性.
主要方法:
- 拟议的语义分区变压器与动态图形网络 (SPD-Net).
- 综合动态图卷积网络 (DGCN) 用于空间相关性,时间卷积网络 (TCN) 用于时间依赖性,和语义分区多头自我注意 (SP-MSA) 用于集中特征提取.
- 引入了一个联合部分映射 (JPM) 模块,用于分层的联合关系分析.
主要成果:
- 与基准数据集上最先进的方法相比,SPD-Net表现优越.
- 在各种步态识别场景中实现了更好的稳定性和准确性.
- 显著降低了计算复杂性,同时保持了关键的步态模式.
结论:
- SPD-Net为步态识别提供了一个强大而高效的解决方案.
- 拟议的语义分区和动态图形网络有效地捕捉复杂的步态动态.
- 这种方法通过增强的人类运动分析来推进生物识别领域.
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