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WebEyeTrack

Created by Eduardo Davalos, Yike Zhang, Namrata Srivastava, Yashvitha Thatigolta, Jorge A. Salas, Sara McFadden, Cho Sun-Joo, Amanda Goodwin, Ashwin TS, and Guatam Biswas from Vanderbilt University

WebEyeTrack is a framework that uses a lightweight CNN-based neural network to predict the (x,y) gaze point on the screen. The framework provides both a Python and JavaScript/TypeScript (client-side) versions to support research/testing and deployment via TS/JS. It performs few-shot gaze estimation by collecting samples on-device to adapt the model to account for unseen persons.

Getting Started

Deciding which version of WebEyeTrack depends on your purpose and target platform. Here is a table to help you determine which version to use:

FeaturePython VersionJavaScript Version
PurposeTraining, Research, and TestingDeployment and Production
Primary Use CaseModel development and experimentationReal-time inference in the browser
Supported DevicesCPU & GPU (desktop/server)CPU (Web browser, mobile)
Model AccessFull access to model internalsOptimized for on-device inference and training
ExtensibilityHighly customizable (e.g., few-shot learning, adaptation)Minimal, focused on performance
FrameworksTensorFlow / KerasTensorFlow.js
Data HandlingDirect access to datasets and logsWebcam stream, UI input

Go to the README (links below) to the corresponding Python/JS version to get stared using these packages.

Acknowledgements

The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A150199 and R305A210347 to Vanderbilt University. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

License

WebEyeTrack is open-sourced under the MIT License, which permits personal, academic, and commercial use with proper attribution. Feel free to use, modify, and distribute the project.

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Real-time Eye-Tracking in the Browser

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