Su Wanzi, Hoad Damon, Pecchia Leandro, Piaggio Davide
School of Engineering, University of Warwick, Library Road, Coventry CV4 7AL, UK.
Warwick Medical School, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK.
Diagnostics (Basel). 2025 Jun 6;15(12):1446. doi: 10.3390/diagnostics15121446.
This study aimed to develop and validate an efficient eye-tracking algorithm suitable for the analysis of images captured in the visible-light spectrum using a smartphone camera. The investigation primarily focused on comparing two algorithms, which were named CHT_TM and CHT_ACM, abbreviated from the core functions: Circular Hough Transform (CHT), Active Contour Models (ACMs), and Template Matching (TM). CHT_TM significantly improved the running speed of the CHT_ACM algorithm, with not much difference in the resource consumption, and improved the accuracy on the x axis. CHT_TM achieved a reduction by 79% of the execution time. CHT_TM performed with an average mean percentage error of 0.34% and 0.95% in the x and y direction across the 19 manually validated videos, compared to 0.81% and 0.85% for CHT_ACM. Different conditions, like manually opening the eyelids with a finger versus without a finger, were also compared across four different tasks. This study shows that applying TM improves the original eye-tracking algorithm with CHT_ACM. The new algorithm has the potential to help the tracking of eye movement, which can facilitate the early screening and diagnosis of neurodegenerative diseases.
本研究旨在开发并验证一种高效的眼动追踪算法，该算法适用于使用智能手机摄像头对在可见光谱中捕获的图像进行分析。该调查主要集中于比较两种算法，它们分别名为CHT_TM和CHT_ACM，是从核心功能：圆形霍夫变换（CHT）、活动轮廓模型（ACM）和模板匹配（TM）缩写而来。CHT_TM显著提高了CHT_ACM算法的运行速度，在资源消耗方面差异不大，并提高了x轴上的准确性。CHT_TM的执行时间减少了79%。在19个经人工验证的视频中，CHT_TM在x和y方向上的平均平均百分比误差分别为0.34%和0.95%，而CHT_ACM分别为0.81%和0.85%。还在四项不同任务中比较了不同条件，如用手指手动睁开眼睑与不用手指睁开眼睑。本研究表明，应用TM改进了原始的CHT_ACM眼动追踪算法。新算法有潜力帮助追踪眼动，这有助于神经退行性疾病的早期筛查和诊断。
