# Multi-Point Displacement Synchronous Monitoring Method for Bridges Based on Computer Vision
## 1 Introduction
It was determined that when monitoring multiple targets, it is necessary to reduce the camera's tilt angle. In 2014, [PERSON] et al. [13] proposed a dynamic displacement monitoring system for railway bridges based on video technology that automatically tracked target points on the basis of the RANSAC algorithm. The system comprised a camera, an optical lens, a lamp, and targets. The system successfully monitored five targets on a railway bridge for one year. In 2015, [PERSON] et al. [14] developed a structural displacement monitoring system based on machine vision, which could extract target displacement in real time from video images. They conducted experiments on two railway bridges and verified the system's applicability to actual bridge monitoring by comparing it with displacement sensors. In 2017, [PERSON] et al. [15] proposed a structural health monitoring system for structural displacement and vibration monitoring. The system used virtual objects to eliminate the dependence of machine vision on the target. The system used an improved feature point extraction algorithm to achieve vibration monitoring of a bridge without targets for the first time. In 2020, [PERSON] et al. [16] proposed a bridge displacement monitoring system using dual cameras for self-motion compensation. The main camera of the system monitored the main target fixed to the structure, and the sub-camera monitored the sub-targets arranged around the system to obtain the system's motion parameters. Finally, the system compensated for the error caused by self-motion. They also verified the applicability of the system through indoor and on-site tests. In 2021, [PERSON] et al. [17] proposed a bridge monitoring method based on multi-camera positioning. This method required the positional relationships of multiple targets captured by different cameras to remain unchanged and then generated a pixel-size and length-size conversion matrix. The displacement of the structure could be calculated on the basis of the target images captured by cameras at any location. In 2020, [PERSON] et al. [18] proposed a structural displacement monitoring method based on camera motion compensation. This method combined image matching and feature detection algorithms to correct errors caused by camera motion. Compared with traditional methods, this method had the advantage of automatically correcting camera motion errors. They also successfully applied this method to the displacement monitoring of a bridge. Computer vision was proposed in the 1950s and gradually entered the industrialization stage in the 1980s. At present, it has become a hot spot for Structural Health Monitoring (SHM) research. Computer vision has a high level of accuracy when used in structural displacement monitoring. It does not require professional technical personnel to operate, and it has outstanding advantages, such as simple equipment installation, a low equipment cost, and an efficient working performance. However, computer vision technology requires installation targets on bridges, and monitoring cameras can obtain only limited point displacement information. The displacement information of limited points cannot accurately reflect the safety status of the structure. Therefore, we propose a multi-point displacement synchronous monitoring method. This method uses the SIFT algorithm in computer vision to generate feature points on the structural surface. These feature points are used as displacement measurement points, and calculating the displacement values of these feature points can obtain structural multi-point displacement monitoring results. This article combined the SIFT algorithm with structural displacement monitoring and obtained the structural multi-point displacement synchronous monitoring results for the first time, providing a new method for applying computer vision in structural deformation monitoring.