# Multi-Point Displacement Synchronous Monitoring Method for Bridges Based on Computer Vision
## 1 Introduction  
In conventional bridge displacement monitoring, sensors are placed on the structure to obtain the structural response and conduct a safety evaluation. The number of monitoring points that can be arranged on the bridge is very limited, making it difficult to evaluate the bridge's safety through the use of local monitoring point data. With the development of computer technology and image acquisition equipment, the displacement monitoring method based on machine vision shows great development potential. This method obtains the monitoring results of the measuring points through feature point extraction, feature point recognition, and feature tracking of the structural image. This method has the outstanding advantages of simple installation, automation, and visualization, as well as being non-contact and having a low cost and high efficiency. Currently, it has become a research highlight in structural deformation monitoring. As early as 1995, [PERSON] et al. [1] used the vision tracking system to measure the mid-span displacement of the Humber Bridge in the U.K. for the first time and compared it with the acceleration sensor. The results showed that visual technology was more suitable for low-frequency and large-displacement structural monitoring, and an accelerometer was more suitable for high-frequency and small-amplitude structural modal monitoring. In1999, [PERSON] et al. [2] proposed applying the computer vision method to the deformation monitoring of railway bridges. They verified the feasibility of this method by monitoring the target point at the mid-span of the bridge. In 2003, [PERSON] et al. [3] used close-range digital photography technology to monitor the vertical deflection of a bridge. They carried out a static load test on the bridge to prove the accuracy of their method. With the further development of image monitoring technology, image-based structural displacement monitoring is no longer limited to single-point monitoring and image post-processing. Relevant research on multi-target and real-time measurements has gradually emerged. In 2006, [PERSON] et al. [4] obtained the real-time displacement of a bridge on the basis of image monitoring technology and used the deformation information to evaluate its bearing capacity. The image monitoring information was used for a deeper structural analysis. In 2016, [PERSON] et al. [5] used upsampled cross correlation (UCC) matching technology to measure the displacement of a three-story frame structure in the laboratory, obtained the displacement monitoring results of multi-point targets on the structure, and extracted the modal parameters of the structure. In 2016, [PERSON] [6] et al. proposed a cross-correlation method to monitor the dynamic displacement of bridges. They collected video data of a pedestrian overpass through a camera and then obtained the displacement and modal information of the bridge using video amplification technology. In 2016, [PERSON] et al. [7,8] combined the Digital Image Correlation (DIC) algorithm with photogrammetry technology to further develop a long-distance bridge deflection system. The system was successfully applied to multi-point deflection monitoring of the Wuhan Yangtze River Bridge. [PERSON] et al. [9,10] proposed a bridge displacement monitoring system based on laser projection, which can monitor multiple target points on the bridge at the same time. The application results from actual bridges showed that the system could accurately obtain the bridges' dynamic and static displacement information. [PERSON] et al. [11,12] proposed a laser-based horizontal displacement monitoring method for foundation pits. They installed a laser transmitter at the monitoring point, projected the laser spot on the screen, and obtained the horizontal displacement of the monitoring point by shooting the change in the spot's position with the camera. The stability, static, and displacement loading tests showed that the method had a high level of accuracy and met the engineering application requirements.