双流互动机制与多模式层次聚合变压器用于步态识别
Jinghang Liu1, Xiangyuan Xu1, Yan Qiu2
1School of Computer Science, Hubei University of Technology, Wuhan, 430000, China.
Scientific reports
|July 18, 2025
概括
步行识别通过GaitSMAT取得了进展,这是一种使用轮和热图数据的新型多式联络方法. 这种方法克服了当前系统的局限性,提高了生物识别的准确性和稳定性.
科学领域:
- 计算机科学 计算机科学
- 生物识别信息 生物识别信息
- 人工智能的人工智能
背景情况:
- 目前的步态识别方法往往是单模,限制了性能.
- 多式联动步态识别在数据集成,融合和时空信息利用方面面临挑战.
- 现有的技术难以捕捉多式联络环境中的远程依赖关系和细粒度动态特征.
研究的目的:
- 提出GaitSMAT,一个用于多式联络步态识别的新型网络.
- 为了解决数据集成,特征融合和时空信息捕获方面的局限性.
- 为了提高识别,利用多式联运步行数据的互补优势.
主要方法:
- GaitSMAT使用双流交互机制 (DSM) 和多模分层聚合变压器 (MHAT) 集成轮和热图数据.
- 通过双向交换和自适应扩展,DSM可实现空间特征交互,捕获远程依赖性,并增强通过双向交换和自适应扩展的表示能力.
- MHAT促进了动态的跨模式特征交互,适应性调节特征的重要性并提高了稳定性.
主要成果:
- 在GREW,Gait3D和SUSTech1K数据集上,GaitSMAT实现了最先进的 (SOTA) 性能.
- 与现有的多式联动步态识别方法相比,显示出显著的改进.
- 在复杂的环境中表现出卓越的稳定性和准确性,特别是在复杂的环境中.
结论:
- GaitSMAT为多式联动步态识别提供了一个全新的技术框架.
- 提出的方法有效地克服了现有的单模式和多模式方法的局限性.
- 为提高步态识别系统的性能和实用性提供了实质性的影响.
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