用视频实时检测眯眼和白内障，助力无障碍网页自适应
Video-Based Detection of squint and cataract for accessibility-aware adaptive web interface rendering
- 通过面部关键点提取眼部几何特征，分类眯眼类型
- 基于灰度与直方图分析晶状体浑浊度，准确率达96.90%
- 可低成本部署于手机或笔记本，适合视障用户界面适配
眯眼和白内障是严重影响视觉感知与交互能力的主要眼部疾病。本文提出一种基于计算机视觉与图像处理的实时视频检测系统，用于自动识别眯眼与白内障。系统采用MediaPipe Face Mesh（478个面部关键点检测模型）提取眼部几何特征，实现多类眯眼分类；同时通过灰度强度与直方图分析晶状体浑浊程度，评估白内障存在性与严重程度。系统仅需标准笔记本或移动设备摄像头录制短时视频，可大规模低成本部署。实验结果显示，眯眼检测准确率为98.39%，白内障分类准确率达96.90%。除自动眼部分析外，该框架还支持视障推断，未来可集成至自适应用户界面与网页无障碍系统中，服务视障人群。
原文摘要 · Abstract (English)
Squint and cataract are major ocular disorders that majorly affect visual perception and interaction capability. This paper proposes a real-time video-based automated detection system for squint and cataract detection based on computer vision and image processing methods. The proposed system uses a media-pipe face-mesh (a 478-point facial landmark detection model) to extract geometric ocular features for multi-class squint classification. Simultaneously, The presence and severity cataract is estimated through grayscale intensity and histogram-based lens opacity analysis. The system records short video sequences with standard laptop or mobile cameras, which can be deployed at low costs and on a large scale. The experimental performance has shown great accuracy in the detection of squint (98.39%) and classification of cataract (96.90%). Besides automatic ocular analysis, the proposed framework is also made accessible for visual impairment inference which will be integrated with future adaptive user interface and Web accessibility systems for people with visual impairment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。