不连续步态图像识别的研究方法基于人类骨关键点提取
1School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种新的步态识别方法,使用人类关键点提取来进行级识别,即使是不连续的图像. 改进后的模型实现了高准确度,在有限的培训数据下证明了稳定性.
科学领域:
- 计算机科学 计算机科学
- 生物识别信息 生物识别信息
- 人工智能的人工智能
背景情况:
- 步态识别利用独特的步行模式进行识别,提供长距离检测和主体合作独立等优势.
- 现有的方法经常与不连续的图像序列作斗争,需要广泛的训练数据.
研究的目的:
- 提出一种强大的步态识别方法,能够从不连续的图像序列中进行级识别.
- 通过尽量减少对时间图像特征的依赖和减少估计错误来增强特征提取.
主要方法:
- 人类关键点提取用于步态分析.
- 一个不连续的选模块来过输入的图像数据.
- 在ResGCN时空图卷积网络中集成交叉阶段部分 (CSP) 连接和XBNBlock,用于特征提取.
主要成果:
- 在CASIA-B步态数据集上实现了79.5%的平均识别准确度.
- 使用有限的训练框架,在CASIA-B上以78.1%的准确度证明了稳健性.
结论:
- 拟议的方法有效地从级,不连续的图像中识别步态.
- 集成CSP和XBNBlock增强了特征提取和模型稳定性,特别是在有限的数据的情况下.
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