人类关节角度估计使用基于深度学习的三维人体姿势估计,用于实际环境中的应用
Jin-Young Choi1, Eunju Ha1, Minji Son2
1Department of Electronic Engineering, Seunghak Campus, Dong-A University, Busan 49315, Republic of Korea.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究使用真实世界的视频解决了3D人体姿势估计 (HPE) 的挑战. 拟议的关节位置校正技术提高了人类活动识别和分析的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 生物机械分析 生物机械分析
背景情况:
- 人体姿势估计 (HPE) 方法,从2D演变为3D,对于AR,动画和监控等应用至关重要.
- 目前的3D HPE方法由于有限的训练集,深度模两可,左/右切换和遮蔽,与现实数据作斗争.
研究的目的:
- 使用现实世界的视频,比较四种3D HPE方法的性能.
- 建议和验证用于改善日常运动中的3DHPE精度的关节位置校正技术.
- 通过纠正的关节角度轨迹来实现直观的人类活动识别.
主要方法:
- 在现实世界的视频数据集上对四个3D HPE算法的比较分析.
- 开发关节位置校正算法,以解决左/右反转和错误检测问题.
- 使用3D人形模拟器,根据纠正的关节角度轨迹识别人类活动.
主要成果:
- 在实际场景中确定了不同3DHPE方法的优缺点.
- 证明了拟议的关节位置校正在减轻常见的HPE错误方面的有效性.
- 创建精确的关节角度轨迹来分析复杂的人类运动.
结论:
- 拟议的联合位置校正显著提高了3DHPE在现实应用中的可靠性.
- 该方法为人类活动识别提供了一个强大的方法,特别是在像体操练习这样的动态场景中.
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