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Robust vision-based sub-pixel level displacement measurement using a complementary strategy
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DOI：10.1016/j.ymssp.2024.111898.png)
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Abstract
En 中文
Over the past decade, computer vision-based measurement approaches have been proposed to achieve non-contact structural displacement measurement. In particular, phase-based optical flow (PBOF) provides accurate dynamic measurements but is limited to estimating small displacements. Conversely, template matching can measure large-scale displacements but suffers from pixel resolution constraints. This study proposes a novel complementary approach that synergizes these two methods, using optical flow to capture high-frequency components of structural movements and reducing drift error with measurements from template matching. First, the video is preprocessed to remove possible lens distortion and image noise. A foreground mask is then generated using the proposed intensity difference-based method to refine the selected region-ofinterest (ROI). Subsequently, structural displacements are estimated using the proposed foreground-weighted normalized cross-correlation (FWNCC) algorithm. An improved phasebased optical flow (IPBOF), which is robust against background clutter and invariant to intrinsic phase wrapping issues, is then used to compute the velocity components. Finally, these two measurements are fused using a complementary filter to achieve sub-pixel level large-scale displacement estimation. The efficacy of the proposed approach was validated with a baseexcited three-story building model in the laboratory. The results demonstrate the method's sub-pixel level accuracy in drift-free large-scale displacement measurement and its robustness against background clutter. Limitations and future research directions are also discussed in the conclusion.
Keywords:
Computer vision
Cross-correlation matching
Phase-based optical flow
Large displacement
Complementary filter
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