A multiple camera position approach for accurate displacement measurement using computer vision

Rolands Kromanis*, Prakash Kripakaran

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

25 Citations (Scopus)
151 Downloads (Pure)

Abstract

Engineers can today capture high-resolution video recordings of bridge movements during routine visual inspections using modern smartphones and compile a historical archive over time. However, the recordings are likely to be from cameras of different makes, placed at varying positions. Previous studies have not explored whether such recordings can support monitoring of bridge condition. This is the focus of this study. It evaluates the feasibility of an imaging approach for condition assessment that is independent of the camera positions used for individual recordings. The proposed approach relies on the premise that spatial relationships between multiple structural features remain the same even when images of the structure are taken from different angles or camera positions. It employs coordinate transformation techniques, which use the identified features, to compute structural displacements from images. The proposed approach is applied to a laboratory beam, subject to static loading under various damage scenarios and recorded using multiple cameras in a range of positions. Results show that the response computed from the recordings are accurate, with 5% discrepancy in computed displacements relative to the mean. The approach is also demonstrated on a full-scale pedestrian suspension bridge. Vertical bridge movements, induced by forced excitations, are collected with two smartphones and an action camera. Analysis of the images shows that the measurement discrepancy in computed displacements is 6%.
Original languageEnglish
Pages (from-to)661-678
Number of pages18
JournalJournal of Civil Structural Health Monitoring
Volume11
Issue number3
Early online date1 Mar 2021
DOIs
Publication statusPublished - 1 Jul 2021

Keywords

  • Computer vision
  • Image processing
  • displacement
  • Signal processing
  • Damage detection
  • Condition assessment
  • Laboratory beam
  • Suspension bridge

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