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Robust vision-based displacement measurement and acceleration estimation using RANSAC and Kalman filter

Earthquake Engineering and Engineering Vibration 2023年第2期22卷 347-358页

作者： Jongbin Won Jong-Woong Park Min-Hyuk Song Youn-Sik Kim Dosoo Moon Department of Civil and Environmental Engineering Chung-Ang UniversityDongjakSeoul 06974Korea Department of Smart Cities Chung-Ang UniversityDongjakSeoul 06974Korea R&D Division Tantan Safety co.LtdSeongdongSeoul 04798Korea Department of Civil and Environmental Engineering University of Hawaii at ManoaHonolulu 96822USA

Computer vision(CV)-based techniques have been widely used in the field of structural health monitoring(SHM)owing to ease of installation and cost-effectiveness for displacement *** paper introduces computer vision based method for robust displacement measurement under occlusion by incorporating random sample consensus(RANSAC).The proposed method uses the Kanade-Lucas-Tomasi(KLT)tracker to extract feature points for tracking,and these feature points are filtered through RANSAC to remove points that are noisy or *** the filtered feature points,the proposed method incorporates Kalman filter to estimate acceleration from velocity and displacement extracted by the *** validation,numerical simulation and experimental validation are *** the simulation,performance of the proposed RANSAC filtering was validated to extract correct displacement out of group of displacements that includes dummy displacement with noise or *** the experiment,both RANSAC filtering and acceleration measurement were validated by partially occluding the target for tracking attached on the *** results demonstrated that the proposed method successfully measures displacement and estimates acceleration as compared to a reference displacement sensor and accelerometer,even under occluded conditions.

关键词： computer vision structural displacement structural acceleration RANSAC Kalman filter

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