通过步态识别人:对共变量影响和挑战的审查
Abdul Basit Mughal1, Rafi Ullah Khan2, Amine Bermak3
1Department of Computer Science, Bahria University, Islamabad P.O. Box 44000, Pakistan.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
人类步行识别使用步行模式进行远程识别,这对于监视至关重要. 本文审查了影响准确性的因素,并将传统方法与深度学习进行比较,以获得更好的系统.
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:
- 人类步行识别可以通过移动和时间特征远程识别个人,这对于保护隐私的视频监控非常有价值.
- 由于视角,服装,速度,遮蔽和照明等共变量,准确的步态识别具有挑战性,在之前的研究中经常被忽视.
- 现有的数据集和方法在解决这些现实世界的复杂性方面存在局限性.
研究的目的:
- 综合审查有效的步态识别方法.
- 评估各种数据集的方法性能,并分析关键共变因子的影响.
- 将传统方法与深度学习技术进行比较,并讨论强大的步态识别的挑战.
主要方法:
- 关于步态识别技术的文献综述.
- 跨多个图像源数据库的性能评估.
- 分析对模型准确性的共同变量影响 (视角,服装,环境).
- 传统与深度学习步态识别方法的比较研究.
主要成果:
- 步行识别的准确性受到共同变量因素的显著影响.
- 深度学习方法有希望,但需要进一步研究共变量稳定性.
- 当前数据集的局限性阻碍了对步态识别系统的全面评估.
结论:
- 对共变量影响的全面理解对于稳健的步态识别至关重要.
- 未来的研究应该专注于开发先进的框架,以应对现实世界的挑战并提高准确性.
- 弥合传统和深度学习方法之间的差距是基于步态的增强生物识别系统的关键.
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