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基于高效和轻量级高分辨率网络 (EL-HRNet) 的人类姿势估计.

Rui Li1,2, An Yan1, Shiqiang Yang1

  • 1School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
概括

一个高效和轻量级的高分辨率网络 (EL-HRNet) 已被开发用于计算机视觉人类姿势估计. 这种模型可以降低参数和计算成本,同时保持对基准数据集的高精度.

关键词:
这就是为什么CBAM是CBAM.人权高官网络 人权高官网络人类姿势估计估计轻量级网络轻量级的网络.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 人类姿势估计是计算机视觉的一个关键领域.
  • 高分辨率网络 (HRNet) 是一种经典的方法,但在计算上昂贵.
  • 在资源有限的设备上部署复杂的模型具有挑战性.

研究的目的:

  • 提出一个改进的,高效的,轻量级的人力资源网络 (EL-HRNet) 用于人类姿势的估计.
  • 为了减少模型复杂性和计算要求.
  • 为了保持高精度的人体姿势估计.

主要方法:

  • 开发了使用点wise和分组卷积的轻量余模块.
  • 集成了卷积区注意力模块 (CBAM) 形成轻量级注意力基本区块 (LA-Basicblock).
  • 对COCO2017和MPII数据集的模型进行了评估.

主要成果:

  • 该EL-HRNet模型有500万个参数和2.0GFlops.
  • 在COCO2017验证集中获得了67.1%的AP分数.
  • 在MPII验证集中获得了87.7%的PCKh@0.5平均值.

结论:

  • 该EL-HRNet模型可显著降低参数和计算成本.
  • 拟议的模型显示了在人类姿势估计中的效率和准确性之间强大的平衡.
  • EL-HRNet适合在资源有限的平台上部署.