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A Keyframe Extraction Method for Assembly Line Operation Videos Based on Optical Flow Estimation and ORB Features.

Xiaoyu Gao1, Hua Xiang1, Tongxi Wang1

  • 1School of Computer Science, Yangtze University, Jingzhou 434023, China.

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This study introduces a new keyframe extraction method for manufacturing videos. It efficiently identifies subtle worker movements, reducing storage needs and improving operational analysis.

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Area of Science:

  • Computer Vision
  • Manufacturing Technology
  • Video Analysis

Background:

  • Assembly line videos are crucial for operational analysis but generate large datasets.
  • Existing keyframe extraction methods struggle with subtle movements and uniform processing.

Purpose of the Study:

  • To develop an adaptive keyframe extraction method for assembly line videos.
  • To improve the efficiency and accuracy of video-based operational analysis.

Main Methods:

  • Combined ORB (Oriented FAST and Rotated BRIEF) features with optical flow estimation (DIS algorithm).
  • Categorized frames by motion intensity and used k-means++ clustering for keyframe selection.
  • Adapted extraction strategies based on action motion amplitudes.

Main Results:

  • Achieved a recall rate of 85.2%, with over 90% recall for minimal movement actions.
  • Demonstrated efficient processing at an average of 274 frames per second.
  • Effectively identified subtle actions and reduced redundant video content.

Conclusions:

  • The proposed method enhances the identification of subtle actions in manufacturing videos.
  • It offers a high-accuracy and efficient solution for video data reduction and analysis.
  • This approach optimizes storage and processing for assembly line monitoring.