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Related Experiment Video

Updated: Dec 6, 2025

Movement Retraining using Real-time Feedback of Performance

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

Marker-Based Structural Displacement Measurement Models with Camera Movement Error Correction Using Image Matching

Jisung Kim¹, Youngdo Jeong², Hyojin Lee³

¹School of Civil, Architectural Engineering & Landscape Architecture, Sungkyunkwan University, Suwon 16419, Korea.

Sensors (Basel, Switzerland)

|October 8, 2020

Summary

This summary is machine-generated.

New models accurately measure structural displacement even at long distances. This overcomes limitations of traditional methods, enhancing construction safety by correcting camera movement errors.

Keywords:

anomaly detection camera movement error error correction image matching marker-based displacement measurement

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Last Updated: Dec 6, 2025

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Area of Science:

Civil Engineering
Geomatics Engineering
Computer Vision

Background:

Traditional marker-based displacement measurement methods struggle with accuracy in outdoor environments over long distances (>50 m) due to camera movements.
Camera movements introduce systematic errors, compromising the reliability of displacement measurements in construction monitoring.

Purpose of the Study:

To develop novel marker-based structural displacement measurement models that correct for camera movement errors.
To enhance the accuracy and reliability of displacement measurements for construction sites, particularly at extended distances.

Main Methods:

Designed and implemented marker-based structural displacement measurement models incorporating image matching and anomaly detection techniques.
Verified model performance by comparing camera movement error correction capabilities against a conventional model.

Main Results:

Successfully corrected systematic errors caused by camera movements, with corrections up to 1.7 degrees.
Achieved a 95% detection rate for markers exhibiting displacement.
Demonstrated a ≥95% probability of error size being less than 10 mm at distances over 100 m (95% confidence interval).
Attained a normalized mean square error of less than 0.1.

Conclusions:

The developed models accurately measure pure object displacement, eliminating systematic errors from camera movements.
These models enable high-accuracy displacement measurements of distant structures using closed-circuit television (CCTV) cameras and markers in outdoor settings.
The findings contribute to improved safety and monitoring in construction through advanced displacement measurement techniques.