vehicle load acts on sensors directly and constantly, resulting in the poor durability, short service life, and high maintenance cost of the system [6,7]. The BWIM method proposed by [PERSON] in 1979, aims to calculate the vehicle weight by measuring the dynamic response of the bridge under the action of moving vehicles [8]. [PERSON] et al. [9] and [PERSON] et al. [10] discussed the standard algorithms of BWIM in detail, and summarized its current progress and critical challenges. Compared with the static weighing and the PWIM methods, the BWIM method has the advantages of lower loss to sensors, longer service life, and no traffic interruption. However, the BWIM method requires the measurement of bridge response by installing sensors at the bridge bottom, which is difficult or inconvenient for cases such as an overpass or river-crossing bridges. Besides, the accuracy of the BWIM method is susceptible to the environment, and only applicable to short- and medium-span bridges. In the past few years, computer vision become a popular technology that has been widely used in various research fields. It has the advantages of low cost, non-contact, ease of setup and operation, and the flexibility of multipoint detection, which is very useful for the temporal and spatial identification of moving vehicles; thus it also provides a new method for vehicle weight identification. [PERSON] et al. [11] proposed an approach to identify the spatio-temporal distribution of traffic loads on bridges, in which the real-time motion trajectory was tracked by the particle filter method and background subtraction. [PERSON] et al. [12] used two digital cameras to measure the deflection of bridge bottom and the vehicle axle information, respectively, and calculate the vehicle weight based on the BWIM algorithm. [PERSON] et al. [13] and [PERSON] et al. [14] identified the moving loads on bridges based on the information fusion of the WIM system and multiple cameras, where the cameras were installed along the bridge to record the real-time positions of the vehicles, and the vehicle weights were identified by the PWIM system at the bridge entrance. As mentioned above, most existing research has only used computer vision for the identification of vehicle temporal-spatial information, whereas the vehicle weight is still based on the PWIM or BWIM methods. Very recently, [PERSON] et al. [15,16] proposed a method to estimate the contact force of each tire and summed the force of all the tires for the vehicle weight. The tire contact force was calculated by multiplying the tire inflation pressure with the tire footprint area, obtained using computer vision techniques. However, the method was based on a few unreasonable assumptions, in which the tire footprint was assumed to be simple shapes, such as rectangular and oval. It is necessary to thoroughly study the relationship between the tire contact force and tire deformation for accurate prediction. Researchers have proposed various theoretical tire models, including but not limited to the Magic Formula [17,18], Kamm circle [19], Nicolas-Comstock [20], average lumped LuGre [21,22], UniTire [23], and Dugoff [24]. Meanwhile, finite element (FE) tire-pavement interaction models were also created to study the tire mechanics. [PERSON] et al. [25] simulated the changes of the tire-road contact stress at static and rolling conditions. [PERSON] et al. [26] developed a three-dimensional (3D) FE tire model validated by the static test data to analyze the tire-pavement interaction. [PERSON] et al. [27] developed a 3D tire-road interaction model to simulate the contact stress at various conditions (i.e., static, free-rolling, acceleration, and braking) considering the unique pattern and materials of the tire. However, the abovementioned theoretical models are relatively complicated and some parameters are difficult to measure in reality, which means they are not practical, while the FE models are mostly used on a specific tire.