提出真实测量浏览器摄像头注视追踪延迟的方法并开源工具。
Measuring Browser Webcam Gaze Honestly: A Capture-Clock Methodology and Open Reference Implementation
- 通过浏览器API恢复每帧捕获时间，避免虚假零延迟
- 实测显示传统方法低估延迟，新方法揭示真实性能差距
- 适合关注在线注视追踪真实性能的研究者与开发者
基于浏览器的摄像头注视追踪被广泛用于大规模数据采集和临床场景，但现有报告的延迟数值可能严重失真。由于通常在输出时打标，而非帧捕获时打标，导致测量延迟几乎恒为0毫秒，无法反映实际引擎速度。本文提出一种诚实测量方法：利用浏览器的requestVideoFrameCallback（rVFC）API获取每帧捕获时间（captureTime），若不可用则使用presentationTime，从而得到可验证的下界。对暴露推理流程的引擎，可通过逐帧队列实现精确配对；对不暴露流程的引擎（如WebGazer），仍能提供更低的延迟下界。我们开源了TypeScript实现与基准测试工具，并在两个可互换的引擎上验证：WebGazer与新的FaceMesh+KRR流水线。
原文摘要 · Abstract (English)
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.
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