## 2 Materials and Methods
### Improved Channel Attention Mechanism Module  
With the deepening of the neural network, the feature map size becomes smaller and smaller, resulting in the weakening or loss of some appearance features and spatial location information of small-sized pedestrians in the image; the relationship between feature channels is not fully considered in the upscaling and downscaling operations of the backbone network. The convolutional channel features correspond to different parts of the human body, which affects the accuracy of pedestrian localization. Therefore, the relationship between body parts and convolutional channels can be effectively utilized through the channel attention mechanism to deal with seriously occluded pedestrians. To address this, in this paper we propose an improved channel attention mechanism to enhance the performance of the ReID branch by extracting more expressive pedestrian apparent features and spatial location information in the feature channels, reducing the number of trajectory interruptions and IDs caused by missed detection or mutual occlusion between pedestrians, and improving the robustness of the multi-pedestrian tracking algorithm. In response to problems, such as loss of pedestrian spatial location information and destruction of image spatial structure caused by global average pooling (GAP) and fully connected (FC) operations in the classical SENet [37] channel attention mechanism, we designed a new module, AM-Block, shown in Figure 4b, which can effectively improve the accuracy of pedestrian localization in the subsequent tracking process.  
The AM-Block structure is shown in Figure 4b, \(F_{A}\) represents the input feature map of the AM-Block; \(W\times H\) is the width and height of \(F_{A}\), and C represents the number of feature channels. After the feature map is input into the AM-Block module, to obtain rich pedestrian location information, the input feature maps are spatially compressed by GAP and global max pooling (GMP) parallel structures to obtain two feature map channel weights. For better characterization of global information, the spatially compressed feature maps are, respectively, dimensionalized by \(1\times 1\) convolution of the number of channels \(C/d\) (it is experimentally known that the best result is obtained when d is taken as 8). Replacing the fully connected operation in the common attention mechanism, the \(1\times 1\) convolution operation does not destroy the spatial structure of the image. Then the number of channels is dimensioned up by the \(1\times 1\) convolution of the number of channels as C, and finally, the feature map after the two branches are summed is subjected to the nonlinear sigmoid activation function to find the channel weights:  
\[F_{1}=f_{1\times 1}^{1}(\delta(f_{1\times 1}^{1}(f_{averagepool}(F_{A})))) \tag{1}\]  
\[F_{2}=f_{1\times 1}^{2}(\delta(f_{1\times 1}^{2}(f_{\text{maxpool}}(F_{A})))) \tag{2}\]  
\[F_{3}=\sigma(F_{1}+F_{2}) \tag{3}\]  
In Equation (1), \(\delta\) represents the ReLU [38] activation function, \(\sigma\) represents the Sigmoid activation function, and \(F_{3}\) represents the generated feature channel weights. Finally, the input feature map \(F_{A}\) is dotted with feature channel weights \(F_{3}\) to obtain the feature map with channel attention \(F_{B}\):  
\[F_{B}=F_{A}\cdot F_{3} \tag{4}\]  
Feature map \(F_{B}\) after the AM-Block module can effectively increase the channel weights of important features and strengthen the important feature information of pedestrians, suppress the interference of useless information, and improve the characterization ability of the backbone feature extraction network.