机器学习分类方法用于轮椅检测,使用视觉词袋技术
Hamid A Jalab1, Ahmad Sami Al-Shamayleh2, Mosleh M Abualhaj3
1Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi Qar, Iraq.
Disability and rehabilitation. Assistive technology
|March 11, 2025
概括
本研究引入了一种机器学习模型,用于使用视觉监控自动检测轮椅,达到98.85%的准确性. 这促进了智能医疗保健和辅助技术的发展,以改善流动性和安全性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 康复工程 康复工程 康复工程
背景情况:
- 轮椅使用者需要在智能医疗环境中提高安全性和可访问性.
- 自主导航和移动支持系统可以从准确的轮椅检测中获得显著的好处.
研究的目的:
- 使用视觉监控开发自动轮椅检测系统.
- 通过增强移动性支持,提高轮椅使用者的安全性和可访问性.
主要方法:
- 开发了一种新的机器学习模型,利用视觉词袋 (BoVWs) 技术.
- 采用了关键特征提取,视觉词汇构建和基于直方图的图像表示.
- 一个支持向量机 (SVM) 分类器被用于图像分类.
主要成果:
- 提出的方法在轮椅检测方面实现了98.85%的高精度.
- 该模型在图像中识别轮椅方面表现出有效性.
结论:
- 对象检测技术显示出识别移动辅助器件的巨大潜力.
- 这项技术可以有助于提高辅助技术应用中的可访问性和安全性.
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