## 1 Introduction  
In the past 40 years, many bridges have been built to serve the public, and 1,079,300 road bridges with a total length of 95,288,200 m [1] had been constructed in China alone by the end of 2023. Meanwhile, recent years have witnessed frequent bridge collapses, overturning, and other accidents, which have attracted great attention from highway maintenance and management departments. The Chinese transportation ministry launched an ambitious plan to install structural health monitoring (SHM) systems for all long-span bridges and special bridges across the sea [2] by the end of 2025, and many SHM systems have been installed [3, 4]. SHM systems are composed of a large number various types of sensors, for example, the of Sutong Bridge SHM systems are composed of 785 sensors [3], and the Stonecutters Bridge SHM systems are consist of 1500 sensors of 20 types. Large quantity and types of sensors, complex data transmission and power supply circuits pose significant challenges to the system's stability and service life.  
Research [5; 6] revealed that the SHM system's ability to identify and locate structural damage is closely related to its density of the measurement points sets, and the damage can be accurately identified only when the sensors are placed on both sides of the damage location but cannot be identified if the sensors are located on only one side of the damage. Therefore, a large number of sensors becomes a prerequisite to locate the damage in each structural component of bridges, which poses great challenge of tremendous cost to bridge owners, as well as huge workload to engineers. As a result, it is an urgent requirement to develop low-cost full-field displacement monitoring technology.  
Machine vision-based motion tracking technology has the advantages of non-contactness, cost-effectiveness, convenience, and high efficiency, and can record continuous temporal and spatial motion of objects at high speed. As the technology is highly compatible with the requirements of structural monitoring and damage identification, it has been widely applied in bridge health monitoring [7; 8; 9; 10; 11; 12; 13; 14]. [PERSON] [8] and [PERSON] [9] systematically investigate various motion tracking methods, including the DIC, feature points matching, optical flow, geometrical matching, etc. Vision-based motion tracking methods are applied in different aspects of bridge measurement [7; 8], for example, influence line of deflection measurement [13], modal information recognition [8], damage identification, cable force estimation, and structural technical condition evaluation [5; 14; 15; 16; 17; 18]. Feature extraction and matching-based motion tracking algorithms suffer from a large number of redundant traversal searches and multi-scale feature computations, and feature mismatching occurs frequently [13; 14], which reduces the accuracy of structure monitoring.  
In this paper, we aim to transfer the geometric properties of large slender ratio member structure and small deformation hypothesis in engineering mechanics to priori knowledge and introduce it into the machine vision-based slender structure deflection monitoring model. A lightweight vison-based model, slice template matching model (STMM), is proposed and verified to measure the full-field temporal-spatial displacement of slender ratio structure by constructing a 1-D template matching model in every pixel column of each structure image frame. This paper is organized as follows: Section 2 introduces the theory of vison-based full-field structural displacement measurement methods and problems encountered; Section 3 proposes the STMM method based on the geometric properties and mechanical priori knowledge of slender structure; Section 4 verifies the STMM method by an indoor multi-box simple support beam experiment; finally, Section 5 provides conclusions and possible perspectives.