Promising target-free approaches include the work of [PERSON] et al. [20] who applied scale invariant feature transform (SIFT) and a subsequent Hough Transform for detection and localization of feature points in circular steel plate structures. [PERSON] et al. [12] used Canny edge detection followed by linear regression for straight-line feature detection on the roof of a full-scale six-story cold-formed steel building. Researchers have noted that geo-reference targets on the periphery of the structure could significantly enhance the accuracy and robustness of the UAV vision-based tracking of structural displacements. Thus, different algorithms have been developed to detect and track reference targets with various geometric patterns. Amongst the most common target patterns, black-and-white checkerboards are widely adopted by researchers. In a recent study, [PERSON] et al. [13] developed a frame-by-frame analysis framework using a sub-pixel edge detector followed by edge point clustering and line regression to localize the target image point with sub-pixel accuracy. For applications of other target patterns, image binarization can be applied for patterns with white dots on the black background [21; 22]. In addition, color-pass filtering has shown robustness in detecting patterns with specific colors [17]. Although research has emerged to advance the use of UAV vision-based structural displacement monitoring during earthquakes, prior studies have at least one of the following limitations:  
* The specimen used for monitoring structural displacements was a reduced-scale structural model rather than a full-scale structure. Compared to a reduced-scale structural model, monitoring the responses of a full-scale structure requires a larger camera-to-scene distance, which may compromise the resolution of the UAV-captured videos. * The study was conducted in a controlled indoor environment, which is different from actual field applications. Factors such as weather-induced UAV drift and additional image noise caused by non-uniform lighting are therefore not able to be considered in the study. * Only one or two types of features and reference target patterns were selected for each study. There is no direct comparison of the accuracy or robustness of different features or patterns when conducting structural displacement monitoring. To address these limitations and to advance the application of UAV vision-based sensing, a framework is proposed for monitoring structural displacements occurring during earthquakes by reprojecting the image points to the 3-D world space based on the world-to-image point correspondences. The tracked structural displacements can provide valuable information in post-disaster safety evaluation and functional recovery analysis of a structure. To provide robust features in the UAV imagery, geo-reference targets with multiple pattern types and colors are installed on a full-scale building specimen, along with the stationary background region. To ensure reliable detection and tracking of multiple image features, a feature point tracking-based algorithm for square checkerboard patterns and a Hough Transform-based algorithm for concentric circular patterns are implemented. Photogrammetry techniques are applied to reconstruct the 3-D world point and extract the structural displacements. Shake table testing, which has been widely adopted in earthquake engineering to support investigations of structural response by physically simulating ground motion excitations, provides a unique opportunity to explore and evaluate vision-based methods for structural displacement monitoring under earthquakes. In this article, the proposed methodology is validated by monitoring the displacements of a full-scale 6-story mass timber building during a series of shake table test programs conducted at the 6-DoF Large High-Performance Outdoor Shake Table (LHPOST6) [23] at UC San Diego.