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    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:

    • 步态识别提供无接触式识别,但与遮蔽 (外套,背包) 和视角变化作斗争.
    • 隐蔽的行走轮和不同的摄像头角度显著降低了识别性能.

    研究的目的:

    • 为了提高步态识别在阻塞和不同摄像头视角下的准确性.
    • 为应对外套 (CL),背包 (BG) 和不同摄像头角度在步态识别中所带来的挑战.

    主要方法:

    • 拟议的步行序列混合 (GSM) 用于数据增强,以减轻因闭塞而导致的信息丢失.
    • 引入多颗粒度特征提取 (MFE) 来捕捉不完整轮的各种步态特征.
    • 开发了特征距离对齐 (FDA) 来改进特征并减少视角差异.

    主要成果:

    • 提出的方法,包括GSM,MFE和FDA,显著提高了步态识别性能.
    • 在CASIA-B和迷你OUMVLP数据集的实验中,与最先进的方法相比,显示出优异的结果.
    • 整合GSM和FDA模块提高了现有的最先进的步态识别技术的准确性.

    结论:

    • 综合方法有效地解决了步态识别中的闭塞和视角挑战.
    • 提出的方法提供了一个可靠的解决方案,可靠的人类识别使用步态分析.
    • 这项工作促进了步态识别在现实世界监视和安全系统中的实际应用.