一个端到端的步态识别系统用于共变条件使用自定义内核CNN CNN.
Babar Ali1, Maryam Bukhari1, Muazzam Maqsood1
1Department of Computer Science, COMSATS University Islamabad, Attock Campus, Pakistan.
Heliyon
|July 18, 2024
概括
本研究引入了一种用于步态识别的深度学习框架,通过专注于动态步行区域,有效处理共变条件. 该方法在识别个体方面取得了很高的准确性,尽管衣服和步行速度有所不同.
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
- 机器学习 机器学习
背景情况:
- 步态识别通过步行模式来识别个人,这对于非侵入性监控非常有用.
- 服装等共变条件显著阻碍了准确的步态识别.
- 当前的方法在不同的环境和外观因素下努力保持性能.
研究的目的:
- 提出一种新的深度学习框架,以应对步态识别中的共同变量挑战.
- 提高步态识别系统在现实场景中的稳定性和准确性.
- 开发一种自动化系统,可以否定手动功能工程的步态分析.
主要方法:
- 开发了一个深度学习框架,在步态分析过程中识别和排除受共变量影响的区域.
- 定制的内核和特征提取专注于动态的,不受共变量影响的区域.
- 一个卷积神经网络 (CNN) 用于特征学习和从拟议区域的个体识别.
- 设计了一个端到端的系统,整合了区域提案和特征提取.
主要成果:
- 拟议的方法实现了90%的准确度,用于识别穿着袋子的人的步态.
- 在穿上外套的受试者中,准确度达到58%,表明对服装变化的强度.
- 对于不同的步行速度,观察到高精度:快速步行94%,慢步行96%.
- 在CASIA数据集A和C上,性能比现有的深度学习方法有所改善.
结论:
- 新的深度学习框架有效地减轻了共变条件对步态识别的影响.
- 该方法提高了基于步态的识别在实际监控应用中的可靠性.
- 与以前的方法相比,自动化系统表现出优越的性能,特别是在具有挑战性的条件下.
相关概念视频
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In the absence...
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01:31Classification of Systems-II
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01:26Classification of Systems-I
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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01:07End Point Prediction: Gran Plot
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For potentiometric titration, the Gran plot is created by plotting...
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