发表机构
Beth Israel Deaconess Medical Center; Harvard Medical School; Department of Biomedical Informatics, Harvard Medical School（贝斯以色列女执事医疗中心; 哈佛医学院; 哈佛医学院生物医学信息学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对浏览器网络摄像头注视追踪器延迟测量失真问题，提出捕获时钟方法，发布开源实现并在WebGazer等引擎上验证，可准确测量真实延迟。
AI 中文摘要
基于浏览器的网络摄像头注视追踪器正越来越多地用于大规模人群数据收集以及实验室眼动仪不适用的临床场景，但报告的延迟数值可能无法代表实际功能。常见做法是在每个注视样本生成时而非其源帧被捕获时为其加时间戳，这会导致无论引擎实际速度多慢，测得的推理延迟都显示约0毫秒。我们展示了如何诚实地测量延迟：通过浏览器的requestVideoFrameCallback（rVFC）API恢复每帧捕获时钟（浏览器为本地相机流公开的captureTime，否则为presentationTime，此时每个恢复的延迟都是可验证的下界）；对于公开推理管道的引擎，通过每帧队列实现精确的源帧配对；对于WebGazer这类不公开推理管道的引擎，则需进一步计算下界。我们发布了开源TypeScript实现及基准测试工具，在WebGazer和新的FaceMesh+KRR管道这两个可互换引擎上进行了演示。
英文摘要
Browser-based webcam gaze trackers are increasingly used for crowd-scale data collection and in clinical settings where lab eye trackers are impractical, but the reported latency numbers may not represent real world functionality. The common practice of timestamping each gaze sample when it is emitted, rather than when its source frame was captured, makes the measured inference latency read about $0\,$ms no matter how slow the engine really is. We show how to measure it honestly, recovering a per-frame capture clock from the browser's \texttt{re\-quest\-Video\-Frame\-Call\-back} (rVFC) API (\texttt{captureTime} where the browser exposes it for local camera streams, else \texttt{presentationTime}, in which case every recovered latency is a verifiable lower bound): exact source-frame pairing through a per-frame queue for engines that expose their inference pipeline, and a further lower bound for engines that do not, such as WebGazer. We release an open TypeScript implementation and benchmark harness, demonstrated on two interchangeable engines: WebGazer and a new FaceMesh+KRR pipeline.
CommentsAccepted at DEMI 2026 (MICCAI 2026 Workshop on Data Engineering in Medical Imaging). Final version to appear in Springer LNCS
