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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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提高小规模的人类动作识别性能使用3D热图体积

Lin Yuan1, Zhen He1, Qiang Wang1

  • 1Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习方法,用于使用伪视频识别细粒度的人类行为. 该方法有效地捕捉了常见的运动特征,提高了小规模数据集的准确性.

关键词:
太极拳是一种行动.细粒度的动作识别.热图体积的热量图的体积.这是一个小规模数据集.

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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
  • 人与计算机的交互

背景情况:

  • 基于骨架的人类行为识别至关重要,但往往侧重于粗的行为.
  • 在深度学习中存在一个差距,用于识别具有实际意义的小规模,细粒度的人类行为.

研究的目的:

  • 开发一种新的深度学习方法,用于细粒度的人类行为识别.
  • 创建一个统一的,适用于不同数据集和模式的通用模型.
  • 为了应对骨架数据中关节不匹配的挑战.

主要方法:

  • 利用基于热图的伪视频来表示人类的行为.
  • 作为特征提取的预先信息,采用人体测量运动学.
  • 开发了一种预训练模型,以捕捉跨数据集的常见运动特征.
  • 将人类骨分成五个部分,以促进信息共享和克服关节不匹配.

主要成果:

  • 预训练的模型有效地捕捉了常见的运动特征.
  • 在各种培训环境中实现稳定而精确的准确性.
  • 在减轻网络过度装配方面已证明有效.
  • 在融合关节和四肢模式特征时,超越了最先进的模型.

结论:

  • 拟议的方法为细粒度的人类行动识别提供了一个强大的解决方案.
  • 该模型在不同的数据集和模式中很好地概括.
  • 利用动力学先验和骨架分区可以提高识别准确性和稳定性.