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从多个姿势估计算法中提取多生物特征,用于交叉视图步态识别.

Ausrukona Ray1, Md Zasim Uddin1, Kamrul Hasan1

  • 1Department of Computer Science and Engineering, Begum Rokeya University, Rangpur 5404, Bangladesh.

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|December 17, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于步态识别的新型多生物识别框架,通过使用多个人类姿势估计算法来增强个人识别. 拟议的方法实现了基于骨架的交叉视图步态识别的最先进性能.

关键词:
在决策层面的核聚变.功能级别的融合融合.步态识别系统可以识别步态.人类姿势估计算法算法剩余图表卷积网络的卷积网络基于骨架的步态识别系统

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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
  • 模式识别 模式识别

背景情况:

  • 步态识别通过走路模式识别个人,这对于远距离识别至关重要.
  • 传统的以外表为基础的方法与现实世界的变化 (服装,物体,照明) 斗争.
  • 使用骨架关键点的基于模型的方法提供了强度,但从历史上看,基于外观的方法表现不佳.

研究的目的:

  • 为了弥合基于骨架和基于外观的步态识别之间的性能差距.
  • 引入一个利用多个人类姿势估计 (HPE) 算法的多生物识别框架.
  • 为了提高基于骨架的步态识别的稳健性和准确性.

主要方法:

  • 使用最先进的HPE算法 (OpenPose,AlphaPose,HRNet) 来从单个视频中生成多样化的骨架数据.
  • 使用剩余图形卷积网络 (ResGCN) 来从骨架数据中提取特征.
  • 实施的特征级融合 (FLF) 和决策级融合 (DLF) 技术.

主要成果:

  • 多生物识别框架证明了基于骨架的超级交叉视图步态识别.
  • 在CASIA-B数据集上实现了最先进的性能.
  • FLF以分点方式汇总特征,而DLF则使用多数票进行最终决策.

结论:

  • 拟议的多生物识别框架有效地提高了基于骨架的步态识别准确性.
  • 利用多个HPE算法和融合技术克服了单源方法的局限性.
  • 这种方法为现实世界步态识别挑战提供了强大的解决方案.