Collection of SHM Case Studies by ASCE EMI Structural Health Monitoring & Control Committee Pixel-Wise Vision-based Approach for Vibration Monitoring from Repurposed Surveillance Videos: Validation on a Full-Scale Building

In this paper, a target-free vision-based vibration monitoring method is used to extract the predominant period of the benchmark full-scale five-story structure tested on the large high performance outdoor shake table at the University of California San Diego as part of the building nonstructural components and systems project. The method uses Kanade-Lucas-Tomasi tracker to extract the pixelbased relative displacement time history of the structure by tracking robust features within video footage recorded by surveillance cameras within the building. Then, the predominant frequency of the structure is estimated using a basic output-only system identification method, frequency domain decomposition. Test Structure and Measured Data The building nonstructural components and systems (BNCS) project consists of a full-scale five-story reinforced-concrete building (see Figure 1) tested at the University of California San Diego. This building was instrumented with analog sensors as well as consumer-level closed-circuit cameras and tested in base-isolated and fixed-base configurations, under different earthquake ground motions. In this study, the videos recorded by the cameras during six independent tests, under six different earthquake ground motions described in Table 1 – downloadable on the NHERI DesignSafe-CI Data Depot [1] – are processed to extract the structural predominant period in the fixed-base configuration. The videos are 25 fps at a resolution of 1280×720, containing “UC San Diego” watermark and black sidebars. More details on the BNCS building and project can be found in Ref. [2]. SHM Methodology and Results Methodology Computer vision-based vibration monitoring methods are widely used to tackle the practical drawbacks of the traditional accelerometer-based measurements. Most of the vision-based monitoring approaches are based on camera installation outside the structure to capture the structural response. Despite good performance of such methods, practical and technical considerations cause some limitations in their general application. To overcome these issues, Harvey and Elisha [3] proposed a vision-based monitoring method using pre-existing surveillance cameras within the buildings. In their method, pre-installed highcontrast grid of points was defined as the target points and the relative pixel displacements were extracted by tracking the points in all the video frames. In the next step, a quadratic mapping was used to convert the structural displacement from pixel coordinates to engineering units. In the present study, a modified version of this method is utilized for vibration monitoring using surveillance cameras. At first, the RGB image sequences are converted to grayscale format which eliminates the hue and saturation information even though the luminance of the images is retained. Then, robust features in the selected region of interest (ROI) in the first image of footage are identified using speeded-up robust features (SURF) algorithm [4]. Therefore, the trackable features are automatically selected and the proposed method is target-free. The features are then tracked for all the remaining frames of the footages using the Kanade-Lucas-Tomasi (KLT) feature-tracking algorithm [5] to extract the relative pixel motions. Note that since the pixel-wise drifts represent the relative motion between the camera attachment