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Non-Invasive Qualitative Vibration Analysis using Event Camera

Dwijay Bane, Anurag Gupta, Manan Suri

Abstract

This technical report investigates the application of event-based vision sensors in non-invasive qualitative vibration analysis, with a particular focus on frequency measurement and motion magnification. Event cameras, with their high temporal resolution and dynamic range, offer promising capabilities for real-time structural assessment and subtle motion analysis. Our study employs cutting-edge event-based vision techniques to explore real-world scenarios in frequency measurement in vibrational analysis and intensity reconstruction for motion magnification. In the former, event-based sensors demonstrated significant potential for real-time structural assessment. However, our work in motion magnification revealed considerable challenges, particularly in scenarios involving stationary cameras and isolated motion.

Non-Invasive Qualitative Vibration Analysis using Event Camera

Abstract

This technical report investigates the application of event-based vision sensors in non-invasive qualitative vibration analysis, with a particular focus on frequency measurement and motion magnification. Event cameras, with their high temporal resolution and dynamic range, offer promising capabilities for real-time structural assessment and subtle motion analysis. Our study employs cutting-edge event-based vision techniques to explore real-world scenarios in frequency measurement in vibrational analysis and intensity reconstruction for motion magnification. In the former, event-based sensors demonstrated significant potential for real-time structural assessment. However, our work in motion magnification revealed considerable challenges, particularly in scenarios involving stationary cameras and isolated motion.

Paper Structure

This paper contains 16 sections, 4 equations, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Event Camera and Data Format
  4. Frequency Estimation Basics
  5. Reconstruction of Intensity Image from Event Stream
  6. Motion Magnification Basics
  7. Complex-Valued Steerable Pyramids Technique for Motion Magnification
  8. Experimental Setup
  9. Hardware Setup
  10. Bias Tuning of Event Sensors
  11. Data Pre-processing
  12. Experiments and Results
  13. Qualitative Evaluation of Frequency Visualization
  14. Qualitative Evaluation of Motion Magnification
  15. Conclusion
  16. ...and 1 more sections

Figures (11)

  • Figure 1: Frame based vs Event Camera, (a.) shows how both sensors capture data of spinning disc and (b.) shows Event Sensor AER encoding, decoding and format. From inbookDavide6865228.
  • Figure 2: Shows event data in 3D (x,y,t) voxel grid on left and its reconstructed intensity image on right. Adapted from rebecq2019highspeedhighdynamic.
  • Figure 3: (a) Frame decomposition using steerable pyramids, (b) Temporal filtering to identify phases in the frequency range we want to magnify, (c) Phase denoising, (d) Magnifying the filtered phases, (e) Reconstructing the frame
  • Figure 4: Hardware Setup for experimentation. It facilitates integration of two Edge Platforms. (a) NVIDIA Jetson Nano (b) Raspberry Pi 3B+ and (c) complete setup with connections.
  • Figure 5: Hyper transition in event data. Adapted from 6696456
  • ...and 6 more figures