## 7 Conclusions  
Vision-based sensing, facilitated by unmanned aerial vehicle (UAV) platforms, has emerged as an effective technology that can enhance life-cycle health monitoring and assessment of civil infrastructures. Such remote, non-destructive measurement approaches offer significant promise in capturing the displacements of structures during natural or manmade hazards. This work aims to improve the robustness of UAV vision-based sensing, focusing on applications of capturing the structural displacements of buildings during earthquakes. In particular, a capture and analysis framework is proposed based on two target detection and tracking algorithms, considering a variety of feature patterns. Patterns in the scene of interest are either square checkerboards or concentric circles, with black-white or red-white color schemes. Several photogrammetry techniques are applied to extract structural displacements, including camera calibration, lens distortion correction, camera pose recovery, and 3-D world point reconstruction. The proposed methodology is evaluated using ground-truth sensor measurements from shake table tests of a full-scale 6-story mass timber building. Geo-referenced targets with various patterns are placed on the building specimen, along with the stationary background region, to provide robust features in the UAV imagery. Statistical analysis of the error characteristics across the various reference target patterns is conducted to investigate the optimal features for use in displacement tracking during earthquakes. The main takeaways from this study include the following:  
* The proposed UAV vision-based method demonstrates reasonable accuracy in tracking structural displacements during a wide range of earthquake motion inputs, with overall root-mean-square errors (RMSEs) at the millimeter level compared to the ground truth measurements from analog sensors. * Given a specific earthquake event and input direction, the average displacement tracking RMSEs are proportional to the achieved peak input accelerations (PIAs). * Based on the statistical analysis of the error characteristics across the various reference target patterns, it is observed that the pattern sizes, pattern shapes, and intensity contrast in the region of interest can affect the accuracy of structural displacement monitoring. Orthogonal-shaped patterns (e.g., straight line intersections or squares) with explicit feature points that can be tracked by the Kanade-Lucas-Tomasi (KLT) algorithm are observed to reduce the error propagation during the video sequence and reduce the displacement measurement noise. Figure 17: Displacement results comparing multiple reference targets at the northeast corner of the roof level (region 2) from the 67% \(\mathrm{MCER}\) Ferndale earthquake test (MID 12). Robustness of the orthogonal-shaped patterns is readily revealed by comparing Type 1–Type 3 to Type 4 patterns. * To characterize the effect of pattern size on the robustness of structural displacement monitoring, a relatively stable RMSE can be observed when the normalized tile dimension (\(D/\Delta_{\text{GT,max}}\)) is greater than 50% for black-and-white checkerboard patterns. Therefore, the actual tile dimension of the black-and-white checkerboard pattern is suggested to be greater than 50% of the peak displacement expected during an earthquake to ensure reasonable accuracy in tracking structural displacements. In summary, this article proposes a methodology to utilize UAV vision-based sensing for monitoring the displacements of full-scale structures during earthquakes. The tracked structural displacements can provide valuable information in post-disaster safety evaluation and functional recovery analysis of the structure. It is worth mentioning that the analysis framework proposed requires post-processing of video data; therefore, the present study does not achieve real-time monitoring during the earthquake event.