## 2 Research Work
### Fusion of Multiple Attention Mechanisms
The weights range from 0 to 1; the larger the weights of a certain channel, indicating that the channel is more important, and finally multiply the weights by the original input feature layer, the completion of the hybrid attention mechanism in the operation of the channel attention mechanism. While [PERSON] et al. in the ECA-Net [36] paper argued that the SENet model will bring some side effects to the prediction of the channel attention mechanism in the hybrid attention mechanism, and capturing the dependencies between all channels is inefficient and useless, the idea of the ECA-Net is to remove the fully_connected layer in the SENet and use the 1D convolution instead, because the 1D convolutional neural network has good cross-channel information acquisition ability. Thus, we adopt the ECA-Net model as the basis to redesign the hybrid attention mechanism in the channel attention mechanism to replace the hybrid attention mechanism in the channel attention mechanism designed by the SENet model. We introduce the single-object tracking algorithm to input the single feature layer of the network, perform the global average pooling operation, and subsequently, the result of the global average pooling is processed by using the 1D convolution. The result of the 1D convolution is processed by a sigmoid function, which can obtain the weights of each channel of the input feature layer, which ranges from 0 to 1, and the weights are multiplied by the original input feature layer. The input of the spatial attention mechanism in the hybrid attention mechanism is the output of the channel attention in the hybrid attention mechanism, which inputs a single feature layer of the network, takes the maximum value and the average value in the channel of each feature point, then stacks the two results, and then reduces the dimensionality of the channel by using the convolution of the channel as 1. Then, the results are obtained by a sigmoid function, which can obtain the input feature layer of each feature point, and the weight of each feature point of the input feature layer can be obtained by a sigmoid function, which ranges from 0 to 1. Finally, the weight is multiplied by the original input feature layer, completing the operation of the spatial attention mechanism in the hybrid attention mechanism. After the above two steps. The improved hybrid attention mechanism operation is completed, and the structure of the improved hybrid attention mechanism is shown in Figure 3.  
Coordinate attention is used to encode precise position information into the neural network, and the core idea is to introduce coordinate information such that the model can better understand the relationship between different positions, as shown in Figure 4.  
Figure 4: Structure of coordinate attention. Figure 3: Improved structure of the hybrid attention mechanism. To enable the network to capture more accurate positional information about the object, we propose connecting the improved hybrid attention mechanism in parallel with coordinate attention, which involves modeling the feature relationship between channel, spatial, and positional information and fusing their results to introduce them into the single-object tracking algorithm, the structure of fusing multiple attention, which is shown in Figure 5. This captures not only cross-channel information but also direction-aware and position-aware information, which can help the algorithm localize and identify the object of interest more accurately. At the same time, it also enables the network to adaptively learn to focus on channel, spatial, coordinate, and other feature information, thus enhancing the feature expression, attention capability, and precision of the entire network. Multiple Attention Mechanism: Input feature \(x\) enters two different branches.