Application Framework and Optimal Features for UAV-Based Earthquake-Induced Structural Displacement Monitoring  
[PERSON]  
1 Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA; 1  
[PERSON]  
1 Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA; 1  
[PERSON]  
2 Qualcomm Institute, University of California San Diego, La Jolla, CA 92093, USA; [EMAIL_ADDRESS] (E.L.); 2  
[PERSON]  
2 Qualcomm Institute, University of California San Diego, La Jolla, CA 92093, USA; [EMAIL_ADDRESS] (E.L.); 2  
[PERSON]  
2 Qualcomm Institute, University of California San Diego, La Jolla, CA 92093, USA; [EMAIL_ADDRESS] (E.L.); 2  
[PERSON]  
1 Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA; 1  
[PERSON]  
3 School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USA; 3  
[PERSON]  
4 Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA; 4  
[PERSON]  
1 Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA; 1  
Footnote 1: email: [EMAIL_ADDRESS]  
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earthquakes and other natural hazards pose a significant challenge to the safety of civil infrastructure. To evaluate post-earthquake structural safety and functional recovery, significant efforts have been spent on earthquake-induced structural displacement tracking using images. In conventional practice, stationary cameras, such as surveillance cameras, have been used to monitor structural motion during earthquakes [10]. With stationary cameras, structural motions including displacements, accelerations, and inter-story drift ratios, can be extracted from images or a series of images (videos) recorded by a regular red-green-blue (RGB) camera or other vision-based sensors [11]. While the conventional practice of using stationary cameras has proven to be useful for displacement tracking of infrastructures, the emergence of commercial unmanned aerial vehicles (UAVs) equipped with on-board cameras offers significantly enhanced flexibility and efficiency for image data collection. These aerial platforms are especially advantageous when it is inconvenient or unsafe to obtain the required camera view from ground-based (stationary) cameras (e.g., the roof of the building [12; 13; 14]). In addition, the motions of a structure in all six degree-of-freedoms (DoFs) can be extracted by combining synchronized video data recorded by multiple UAV platforms obtained from locations surrounding the structure [14]. Although the promise of UAV-based methods is recognized, the accuracy and robustness of UAV vision-based methods to monitor structural displacements are significantly affected by (a) the correction of UAV drift-induced camera movement and (b) the robustness of detection for representative image features for tracked objects. Unlike ground-based cameras, which can be assumed to be stationary, the movement of on-board cameras caused by UAV drift cannot be neglected for UAV vision-based approaches. Therefore, multiple methods have been deployed in practice for camera pose recovery to avoid possible image misalignment. Conventional non-learning-based methods include homography transformation [12; 15; 16], direct linear transformation (DLT) [17], and embedded inertial measuring unit (IMU) measurement [18]. Learning-based methods are also applied in recent studies. For example, [PERSON] et al. [19] proposed a two-stage correction method to eliminate UAV drift-induced measurement error using stationary points and variational mode decomposition (VMD). To enhance the reliable detection and tracking of objects, both reference target-based and target-free methods have been utilized in UAV vision-based monitoring to improve the tracking accuracy of structural displacements.