## 1 Introduction  
With the elapse of time, it is inevitable for a bridge to face structural degradation under the long-term effects of natural factors (e.g. climate and environment). In extreme cases, the bridge structure will suffer from catastrophic damages with the continuous increase in traffic volume and heavy vehicles, due to the booming economy [1]. The traditional approach of structural management, which is mainly manual periodic inspection, can no longer satisfy the demand of modern transport facilities. The traditional approach is inefficient, uncertain, and highly subjective, lacking in scientific or quantitative bases [2; 3; 4; 5]. Structural health monitoring (SHM) aims to monitor, analyze, and identify all kinds of loads and structural responses during the service life of the target structure, to realize the evaluation of its structural performance and safety status, and provide support for the propriator in structural management and making maintenance decisions [6; 7; 8]. In order to achieve this goal, a new sensor technology must be developed in combination with interdisciplinary theories and methods to provide advanced monitoring methods and reliable data sources for SHM [9; 10; 11]. Displacement is an important index of structural state and performance evaluations [7]. The static and dynamic characteristics of a structure, such as bearing capacity [12], deflection [13], deformation [14], load distribution [15], load input [16], influence line [17], influence surface [18], and modal parameters [19; 20], can be calculated by structural displacement, to convert them further into physical indicators of response for structural safety assessment. Since the 1990s, SHM systems have been set up on important large-span bridges across the globe. The main functions are to monitor the state and behavior of the bridge structure, while tracking and recording environmental conditions. On the upside, these systems have high local accuracy, run on an intelligent system, and support long-term continuous observation. On the downside, these systems are too costly to construct, the sensors cannot be calibrated periodically, and the layout of monitoring points is limited by a local terrain and the structure type. The geometric deformation of the bridge structure can only be collected by a few discrete monitoring points, making it difficult to characterize the local or global holographic geometry of bridge safety [21; 22; 23; 24; 25]. With the continuous development of machine vision technology and image acquisition equipment, structural displacement monitoring methods based on computer vision continues to emerge and have been verified in practical engineering applications [26; 27; 28; 29; 30]. Given its long-distance, noncontact, high-precision, time-saving, labor-saving, multi-point detection, and many other advantages, as well as increasing attention has been received from scientific researchers and engineers [31]. This method is mainly used to track the target of the measured structure video, which is captured by the camera, to obtain the moving track of the measuring point in the image, and then determine the displacement information of the structure through the set relationship between the image and the real world. The camera is mounted on a fixed point which is far from the structure to be tested, eliminating the requirement to install a fixed support point on the structure for the contact displacement detection method. In addition, low-cost multi-point measurement it is easy to achieve because the camera field of view can cover multiple measurement areas of the structure. Structural displacement monitoring based on computer vision has been applied in many tasks of bridge health monitoring, such as bridge deflection measurement [32], bridge alignment [33], bearing capacity evaluation [34], finite element model calibration [35], modal analysis [36], damage identification [18], cable force detection [29], and dynamic weighing assistance [12].