基于改进的囊网络的头部姿势估计的注意引导的休伯损失
Runhao Zhong1, Li He1, Hongwei Wang1
1School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一种使用残余和囊网络的新型头部姿势估计模型. 改进后的模型通过保留空间结构信息,提高了准确性,特别是在具有遮的复杂场景中.
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:
- 头部姿势估计对于人类行为分析至关重要,应用于HCI和疲劳检测.
- 传统的方法在复杂的场景中与空间结构损失作斗争,如遮蔽,导致精度低.
研究的目的:
- 开发一个强大的头部姿势估计模型,克服现有方法的局限性.
- 提高精度和空间结构的保存,特别是在具有挑战性的环境中.
主要方法:
- 一个深度残余网络提取多层次的特征,通过全球关注块增强.
- 一个改进的囊网络与自我注意路由编码和传输功能,保存空间信息.
- 优化的Huber损失被用于改善模型的稳定性.
主要成果:
- 拟议的模型在公共数据集 (300W-LP,AFLW2000,BIWI) 上实现了最先进的性能.
- 观察到精度显著提高,特别是在涉及遮的场景中.
结论:
- 新的基于残留和囊网络的方法有效地解决了头部姿势估计中的空间结构损失.
- 该模型表现出卓越的稳定性和准确性,特别是在复杂的现实世界条件下.
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