# Multi-View Gait Recognition Based on a Siamese Vision Transformer
## 2 Related Work  
At present, there are many methods of solving the problem of multi-view gait recognition. Some researchers adopt the method of constructing a three-dimensional model and use the close cooperation of multiple cameras to construct a three-dimensional model of pedestrian movement, so as to weaken the influence of multiple perspectives, clothing, and other factors. [PERSON] et al. proposed combining arbitrary views taken by multiple cameras to construct appropriate images that match the training view for pedestrian gait recognition [9]. [PERSON] et al. constructed a correlation energy map between their proposed generative gait model and the data, and adopted a dynamic programming method to select possible paths and extract gait kinematic trajectory features, proposing that the extracted features were inherent to the 3D data [10]. In addition, [PERSON] et al. set out a comprehensive approach that combines the probability information related to 3D human poses with convolutional neural networks (CNNs), and introduced a unified formula to address the challenge of estimating 3D human poses from a single RGB image [11]. In subsequent research, [PERSON] et al. changed the extraction method of human 3D pose modeling, and proposed a deformable pose ergodic convolution to optimize the convolution kernel of each joint by considering context joints with different weights [12]. However, this method of 3D pose modeling is more complicated to calculate and has high requirements regarding the number of cameras and the shooting environment, so it is difficult to use in application settings with ordinary cameras. Some scholars used the view transformation model (VTM) to extract the frequency domain features of gait contours by transforming them from different views. For instance, [PERSON] et al. proposed a gait recognition method based on frequency domain features and view transformation. First, a spatio-temporal contour set of gait characteristics was constructed, and the periodic characteristics of gait were subjected to the Fourier analysis to extract the frequency domain features of pedestrian gait; the multi-view training set was used to calculate the view transformation model [13]. In this method, the Spatio-temporal gait images in the gait cycle are usually first fused into a gait energy image (GEI), which is a Spatio-temporal gait representation method first proposed by [PERSON] et al. [14]. [PERSON] et al. combined the gait energy image (GEI) with the view transition model (VTM), and used a linear discriminant analysis (LDA) to optimize the feature vectors and improve the performance of VTM [15]. Later, [PERSON] et al. used a motion clustering method to classify gaits from different views into groups according to their correlation; within each group, a canonical correlation analysis (CCA) was used to further enhance the linear correlation between gaits from different views [16]. In addition, researchers have considered how to perform gait recognition from any view. [PERSON] et al. proposed a viewpoint invariant discriminant projection (ViDP) method to improve the discrimination accuracy of gait features using linear projection [17]. However, most of these methods are realized by domain transformation or singular value decomposition, and the perspective of transformation is complicated. Others have used an adversarial generative network to normalize multiple views into a common perspective. [PERSON] et al. proposed a perspective-shifting adversarial generative network (VT-GAN), which can transform gait views across two arbitrary views with only one model [18]. [PERSON] et al. designed GaitGANv1 and GaitGANv2, versions of a gait adversarial generation network, which use GAN as a regressor to generate a standardized side view of a normal gait; this not only prevents the falsification of gait images, but also helps to maintain identity information, and the networks achieved good results in cross-view gait recognition [19,20]. In addition, [PERSON] et al.