Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Developmental Neurotoxicity of Short-Chain Phthalates in Human Neurospheres.

Environmental toxicology and pharmacology·2026
Same author

Decision-Aware Multi-Horizon Fault Prediction for Photovoltaic Inverters: Analysis of Threshold-Based Alarm Policies Under Operational Constraints.

Sensors (Basel, Switzerland)·2026
Same author

Vitreous humor-mimetic liposomes enable effective gene delivery to the eye via intravitreal injection.

Biomaterials·2026
Same author

Current levels and needs of digital competence among medical teachers in Uzbekistan: a multi-institutional assessment and future directions.

BMC medical education·2026
Same author

Viral Infection-Inspired Autonomous Detection of Fusion-Competent Viruses for Screening and Environmental Surveillance.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Sarcopenia Predicts Outcome After Chemoimmunotherapy, Not Chemotherapy, in Advanced Lung Cancer: Single-Centre Retrospective Study.

Journal of cachexia, sarcopenia and muscle·2026

Related Experiment Video

Updated: Dec 6, 2025

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.7K

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
PubMed
Summary

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 detectioncamera movement errorerror correctionimage matchingmarker-based displacement measurement

More Related Videos

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
07:51

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

17.1K
Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
06:09

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Published on: March 12, 2021

3.6K

Related Experiment Videos

Last Updated: Dec 6, 2025

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.7K
Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
07:51

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

17.1K
Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
06:09

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Published on: March 12, 2021

3.6K

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.