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Updated: Nov 18, 2025

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Intelligent Video Highlights Generation with Front-Camera Emotion Sensing.

Hugo Meyer1, Peter Wei1, Xiaofan Jiang1

  • 1Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.

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|February 6, 2021
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Summary
This summary is machine-generated.

HOMER, a cloud-based system, generates video highlights by combining video content with user emotions. This approach significantly improves highlight detection accuracy compared to existing methods.

Keywords:
emotion recognitionimage processingmobile computingsignal processing algorithms

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Automated video highlight generation is challenging due to the subjective nature of identifying significant moments.
  • Current methods primarily rely on video content analysis, often neglecting the crucial role of human emotion in defining 'highlights'.
  • There is a need for more relevant and flexible video segmentation systems that can capture subjective user preferences.

Purpose of the Study:

  • To introduce HOMER, a novel cloud-based system for automated video highlight generation.
  • To enhance video summarization by integrating user emotion data with internal video features.
  • To demonstrate the system's effectiveness and adaptability across diverse video types and user scenarios.

Main Methods:

  • Developed a cloud-based system (HOMER) for video segmentation and highlight generation.
  • Fused internal video content-based features with user emotion data to identify highlights.
  • Conducted experiments using videos from a dual camera setup and the Microsoft Video Titles in the Wild (VTW) dataset.
  • Implemented smartphone applications to showcase portability and scalability.

Main Results:

  • HOMER outperforms state-of-the-art solutions in video highlight generation.
  • Achieved up to a 38% improvement in F1-score compared to baseline methods.
  • Demonstrated effective highlight detection by leveraging human emotions, addressing the subjective nature of the task.
  • Validated the system's performance on diverse video datasets without requiring external hardware.

Conclusions:

  • HOMER offers a significant advancement in automated video highlight generation by incorporating affective data.
  • The system provides a relevant, flexible, and accurate solution for video segmentation.
  • The successful implementation in smartphone applications highlights HOMER's portability and scalability for real-world use.