一项关于对抗阻塞的步态识别的调查:分类学,数据集和方法
Tianhao Li1,2, Weizhi Ma1, Yujia Zheng1,3
1School of Information Science and Technology, North China University of Technology, Beijing, China.
PeerJ. Computer science
|February 3, 2025
概括
步态识别通过行走模式来识别人,但阻塞是一个问题. 这项研究调查了克服封闭挑战的方法,以实现现实世界的人类识别.
科学领域:
- 计算机视觉 计算机视觉
- 生物识别信息 生物识别信息
- 模式识别 模式识别
背景情况:
- 传统的生物识别需要主体合作,限制了实际使用.
- 步态识别通过行走模式分析提供非合作性人类识别.
- 阻塞对现实世界步态识别系统构成重大挑战.
研究的目的:
- 系统地分类现实世界的阻塞类型,数据集和阻塞步态识别的方法.
- 进行对现有研究进行全面的调查,以解决阻塞下的步态识别问题.
- 确定未来的研究方向,以推进封闭的步态识别.
主要方法:
- 开发一种新的分类学来分类封闭的步态识别场景.
- 广泛的文献综述,重点关注数据集和隐蔽步行识别方法.
- 分析当前面临的挑战和在现场提出的解决方案.
主要成果:
- 闭塞类型的结构化分类及其对步态识别的影响.
- 一个数据集的概述,专门设计用于封闭的步态识别研究.
- 一个综述的各种技术开发处理封闭的步态数据.
结论:
- 阻塞是阻碍步态识别在现实世界中部署的一个关键因素.
- 拟议的分类学为理解和应对封闭挑战提供了一个框架.
- 需要进一步的研究来提高步态识别在不受约束的环境中的稳定性和适用性.
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