## 3 Our Approach
### Multi-Feature Fusion Algorithm  
Generally speaking, when the lighting is not strong, color features are more dominant, but have a stronger impact on viewpoint changes than other features. Based on texture features, contour boundaries can be easily processed. Therefore, this method selects color and texture features to extract the most obvious appearance and posture contours to distinguish the identities of different pedestrians.  
To utilize low-level and high-level information for pedestrian re-identification, this article introduces a multi-feature fusion mechanism. The module network structure is shown in Figure 4, consisting of a backbone network for feature map extraction and multiple sub-networks for shallow feature processing. The backbone network uses the ResNet-50 network with its multi-level architecture. The shallow feature maps tend to capture detailed information, while the deep feature maps tend to present global information. The widely used SoftMax classifier obtains shallow feature maps from different layers of the backbone network and places them in the feature space. Then, the extracted deep and shallow features are connected as the joint features of pedestrians, which together form a label loss function as binary and unary terms to prepare for obtaining the appropriate image-level labels.  
Assuming \(X=\left\{x_{1},x_{2},...,x_{n}\right\}\) is the available dataset, where \(x\) is the training image. Let \(R=\left\{\xi\left|\xi\in R^{N}\right.\right\}\) represent the sample space. For the given feature spaces A and B, the two feature vectors \(T_{1}\) and \(T_{2}\) extracted from the same image \(\xi\) can be represented as \(A=\left\{T_{1}\left|T_{1}\in R_{P}\right.\right\}\) and \(B=\left\{T_{2}\left|T_{2}\in R_{q}\right.\right\}\).  
For a given detection image \(x_{P}\), extract a d-dimensional feature vector and use Equation (4) to represent it.  
\[x=\left(x_{1},x_{2},\ldots,x_{d}\right)\in\mathbb{R}^{d} \tag{4}\]  
The combined feature vectors are represented by Equation (5).  
\[U=\left\{\left\{x_{i},\ldots,x_{n}\right\},\left\{x_{j},\ldots,x_{n}\right\}, \left\{x_{d},\ldots,x_{n}\right\}\right\} \tag{5}\]  
The similarity between the two image samples can be determined based on the Mahalanobis distance [51], calculated using Equation (6).  
\[W_{ij}=exp(-\left(x_{i}-x_{j}\right)^{T}M(x_{i}-x_{j})) \tag{6}\]  
The binary term is defined using Equation (7).  
\[\psi(y_{i},y_{j}\Big{|}x_{i};x_{j})=\zeta(y_{i},y_{j})Y_{i}^{y_{i}}Y_{j}^{y_{ j}}W_{ij} \tag{7}\]  
Similar to unary terms, binary terms are also limited by the package-level annotations \(y_{i}\) and \(y_{j}\). Binary terms can provide important knowledge that cannot be captured by unary terms (such as structural context dependencies). The Potts model provides a simple label compatibility function \(\zeta\Big{(}y_{i}y_{j}\Big{)}\in\)[0, 1] in Equation (7), represented by Equation (8).  
\[\zeta(y_{i},y_{j})=\begin{cases}0,&y_{i}=y_{j}\\ 1,&\text{otherwise}\end{cases} \tag{8}\]  
It introduces penalties for assigning similar images with different labels.  
By minimizing the label loss of (2), the pseudo-image-level labels i of a human image i can be obtained, and once such labels are generated, they will be used to update the network parameters.