通过等级补丁描述器识别人类行为,并通过局部受约束的线性编码进行估计
Lina Liu1,2, Kevin I-Kai Wang2, Biao Tian3
1College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种使用层次补丁描述器 (HPD) 和近似局部受约束线性编码 (ALLC) 的新型人类行为识别方法. 该方法在识别各种数据集中的人类行为方面实现了高准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人类行为识别对于智能监控和人机交互至关重要.
- 现有的方法经常面临效率和准确性方面的挑战.
研究的目的:
- 提出一种高效准确的人类行为识别方法.
- 将详细的局部特征描述与快速编码技术相结合.
主要方法:
- 计算能量图像物种用于全球人类行为描述.
- 使用空间金字塔匹配进行详细描述的结构层次补丁描述器 (HPD).
- 采用局部约束的近似线性编码 (ALLC) 进行高效的补丁编码.
主要成果:
- 在维兹曼和DHA数据集上实现了高识别精度.
- 与特定的能量图像物种结合HPD和ALLC证明了卓越的性能.
- 具体准确率:100% (MHI),98.77% (MEI),93.28% (AMEI),94.68% (EMEI),95.62% (MEnI) 的. 这是一个很好的方法.
结论:
- 拟议的HPD和ALLC方法为人类行为识别提供了一个计算效率高,准确的解决方案.
- 全局和详细的特征描述的组合提高了识别性能.
- 该方法显示出在智能监控和人机交互方面的应用潜力很大.
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