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Related Experiment Video

Updated: Jul 11, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
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Automatic Gaze Analysis: A Survey of Deep Learning Based Approaches.

Shreya Ghosh, Abhinav Dhall, Munawar Hayat

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 15, 2023
    PubMed
    Summary

    Automatic eye gaze analysis remains challenging due to various factors. Future research must focus on unconstrained environments and less supervision for robust, real-world applications in computer vision and HCI.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction (HCI)

    Background:

    • Automatic gaze analysis faces challenges from individual eye appearance variations, head movements, occlusions, and varying image quality.
    • Key open questions involve identifying crucial gaze cues in unconstrained settings and real-time encoding methods.

    Approach:

    • This review analyzes recent advancements in gaze estimation and segmentation, with a focus on unsupervised and weakly supervised learning techniques.
    • Methods are evaluated based on advantages and reported metrics, highlighting challenges in unconstrained environments.

    Key Points:

    • Robust and generic gaze analysis requires addressing real-world complexities like unconstrained setups.
    • Learning with limited supervision is a critical area for developing more adaptable gaze analysis systems.

    Conclusions:

    • Future research should prioritize developing real-world gaze analysis systems capable of unconstrained operation.
    • Advancements in gaze analysis have broad applicability in Computer Vision, Augmented Reality (AR), Virtual Reality (VR), and HCI.