• Smart Structures and Systems
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• v.31 no.4
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• pp.409-419
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• 2023

Structural integrity can be accessed from dynamic deformations of structures. Moreover, dynamic deformations can be acquired from non-contact sensors such as video cameras. Kanade-Lucas-Tomasi (KLT) algorithm is one of the commonly used methods for motion tracking. However, averaging throughout the extracted features would induce bias in the measurement. In addition, pixel-wise measurements can be converted to physical units through camera intrinsic. Still, the depth information is unreachable without prior knowledge of the space information. The assigned homogeneous coordinates would then mismatch manually selected feature points, resulting in measurement errors during coordinate transformation. In this study, a two-stage optimization method for video-based measurements is proposed. The manually selected feature points are first optimized by minimizing the errors compared with the homogeneous coordinate. Then, the optimized points are utilized for the KLT algorithm to extract displacements through inverse projection. Two additional criteria are employed to eliminate outliers from KLT, resulting in more reliable displacement responses. The second-stage optimization subsequently fine-tunes the geometry of the selected coordinates. The optimization process also considers the number of interpolation points at different depths of an image to reduce the effect of out-of-plane motions. As a result, the proposed method is numerically investigated by using a truss bridge as a physics-based graphic model (PBGM) to extract high-accuracy displacements from recorded videos under various capturing angles and structural conditions.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.93-97
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• 2010

This paper presents the improved KLT(Kanade-Lucas-Tomasi) of registration of Image sequence captured by camera mounted on unmanned helicopter assuming without camera attitude information. It consists of following procedures for the proposed image registration. The initial interested points are detected by characteristic curve matching via dynamic programming which has been used for detecting and tracking corner points thorough image sequence. Outliers of tracked points are then removed by using Random Sample And Consensus(RANSAC) robust estimation and all remained corner points are classified as inliers by homography algorithm. The rectified images are then resampled by bilinear interpolation. Experiment shows that our method can make the suitable registration of image sequence with large motion.