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Pixel-Level and Robust Vibration Source Sensing in High-Frame-Rate Video Analysis.

Mingjun Jiang1, Tadayoshi Aoyama2, Takeshi Takaki3

  • 1Department of System Cybernetics, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan. m-jiang@robotics.hiroshima-u.ac.jp.

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This study shows that vibration feature extraction using pixel-level digital filters is effective for tracking vibrating objects in high-frame-rate videos, even with appearance variations. Dynamics-based features prove robust against diverse imaging conditions.

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drone trackinghigh-frame-rate videoobject trackingpixel-level digital filtersvibration source localization

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Area of Science:

  • Computer Vision
  • Image Processing
  • Robotics

Background:

  • Conventional object tracking relies on spatial pattern recognition in high-quality images.
  • Vibration feature extraction from high-frame-rate videos presents unique challenges due to appearance variations.
  • Robust tracking of dynamic objects, like rotating machinery, requires advanced techniques.

Purpose of the Study:

  • To investigate the impact of appearance variations on vibration feature extraction detectability.
  • To evaluate the robustness of dynamics-based vibration features against diverse imaging and object parameters.
  • To demonstrate the effectiveness of pixel-level digital filters for vibrating object tracking.

Main Methods:

  • Utilized high-frame-rate (2000 fps) videos of rotating fans with varying positions, orientations, and lens settings.
  • Applied pixel-level digital filters to extract vibrating regions.
  • Assessed feature robustness against changes in aperture, focal condition, object size/orientation, rotational frequency, and background complexity.
  • Conducted tracking experiments on a flying multicopter in outdoor scenarios.

Main Results:

  • Pixel-level digital filters successfully extracted vibrating regions across various appearance conditions.
  • Dynamics-based vibration features demonstrated significant robustness against changes in camera lens settings and object dynamics.
  • Successful tracking was achieved even with complex backgrounds and challenging outdoor imaging conditions.

Conclusions:

  • Pixel-level digital filters offer a robust method for vibration feature extraction in high-frame-rate videos.
  • Dynamics-based features are effective for tracking vibrating objects despite appearance variations and complex environments.
  • The approach shows promise for applications requiring reliable tracking of dynamic machinery and vehicles.