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Related Experiment Video

Updated: Oct 18, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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VideoModerator: A Risk-aware Framework for Multimodal Video Moderation in E-Commerce.

Tan Tang, Yanhong Wu, Yingcai Wu

    IEEE Transactions on Visualization and Computer Graphics
    |September 29, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Video moderation is streamlined with VideoModerator, a risk-aware framework. It combines machine learning and interactive visualizations to efficiently detect and review deviant content in e-commerce livestreams.

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    Last Updated: Oct 18, 2025

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.2K

    Area of Science:

    • Computer Science
    • Human-Computer Interaction

    Background:

    • E-commerce livestreaming necessitates robust video moderation to remove deviant or explicit content.
    • Manual review of multimodal video content (frames, audio) is time-consuming and challenging.

    Purpose of the Study:

    • To develop an effective video moderation framework integrating human expertise and machine learning insights.
    • To enhance the efficiency and accuracy of detecting potentially deviant content in livestreams.

    Main Methods:

    • Proposed VideoModerator, a risk-aware framework utilizing advanced machine learning models for feature extraction.
    • Developed an interactive visualization interface with segmented timelines, risk-aware visual summarization, and storyline-based audio exploration.
    • Integrated human knowledge with machine insights for comprehensive content analysis.

    Main Results:

    • The framework successfully extracts risk-aware features from multimodal video content.
    • Interactive visualization aids in quick navigation and identification of high-risk periods and content.
    • Case scenarios, experiments, and user studies validated the effectiveness of VideoModerator.

    Conclusions:

    • VideoModerator offers an efficient and effective solution for multimodal video moderation in e-commerce.
    • The integration of risk-aware machine learning and interactive visualization significantly improves moderation processes.