基于智能手机的距离估计方法的准确性和可用性,用于视觉辅助技术开发
Giles Hamilton-Fletcher1,2, Mingxin Liu1, Diwei Sheng3
1Department of Ophthalmology, NYU Grossman School of Medicine, NYU Langone HealthNew York University New York NY 10017 USA.
IEEE open journal of engineering in medicine and biology
|March 15, 2024
概括
这项研究评估了对盲人或视力低下者 (PBLV) 的智能手机距离估计. 激光雷达和ARKit显示出最好的准确性,而CoreML提供了良好的可用性,指导未来的辅助技术设计.
科学领域:
- 人与计算机的交互
- 计算机视觉 计算机视觉
- 辅助技术 辅助技术 辅助技术
背景情况:
- 盲人或视力低下者 (PBLV) 需要智能手机应用程序提供准确的距离信息.
- 现有的距离估计技术缺乏对准确性和可用性的系统评估.
研究的目的:
- 系统地评估五种基于智能手机的PBLV距离估计方法的准确性和可用性.
- 为设计可靠的视觉辅助技术提供信息.
主要方法:
- 测试了五种方法:CoreML,IR_self,LiDAR_back,ARKit_self,以及ARKit_back. 这三种方法都是使用的.
- 在图像中心和边缘在1-3米处评估的性能.
- 评估准确性 (平均误差) 和可用性指标 (CPU/电池使用量,视野).
主要成果:
- 在图像中心,LiDAR_back和ARKit方法实现了<±2.5厘米的平均误差.
- 核心ML和IR_self显示了最高的外围误差 (±41厘米和±32厘米).
- 核心ML表现出良好的可用性,低资源消耗和广的视野.
结论:
- 在辅助应用中,LiDAR和ARKit对准确的距离估计具有前景.
- 核心ML提供了一个可用的替代方案,特别是当专用传感器无法使用时.
- 调查结果为开发有效的智能手机辅助工具提供了关键数据.
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