## 4 Object-Aware Adaptive Convolution Kernel Attention Mechanism
### Object-Aware Convolution Kernel Attention Mechanism  
Based on the analysis presented in this work, it is evident that the convolution kernels activated by different objects are different. The weighted selection of convolution kernels cannot be performed by the attention module used in ordinary end-to-end training. In order to effectively address this problem, we combine the characteristics of object tracking tasks and propose a new convolution kernel attention module for each individual object. This attention mechanism is different from the attention module traditionally applied during the training phase. The attention weight acquisition is not based on the training phase of the Siamese network. Instead, one-time training is performed at the beginning of the object tracking task. (1) At the beginning of the object tracking task, the object template is generated using the object position given in the initial frame, and the object template convolution feature is obtained by using the template branch of the Siamese network. (2) At the original object location, the search area is divided in the initial frame according to the size of the object search area set by the network. The candidate area branch of the Siamese network is used to obtain the convolution features of the candidate area. (3) The object template convolution feature and the candidate area convolution feature are both matched with each convolution kernel according to the template. At this time, the convolution kernel  
Figure 7: The results of SE attention module for each convolution kernel (”soccer” video sequence). response maps with different response levels can be obtained. (4) Finally, based on the peak in each response map at the object position, we evaluate the support of the activated convolution kernel for the tracking task and set the convolution kernel weight used in this tracking. At the beginning of each tracking task, the above process is repeated, and different convolution kernel weight distributions are obtained based on different objects. The algorithm is shown in Table 1.  
\[psr=\frac{p_{x_{0},y_{0}}-\mu_{s}}{\sigma_{s}} \tag{2}\]  
where \(x_{0},y_{0}\) represent the coordinates of the peak in the response map, and \(p_{x_{0},y_{0}}\) represents the specific value of the peak. With the peak coordinate at the center, the area with radius \(r\) is called the peak area. The region outside the peak area is called the sidelobe area. \(\mu_{s}\) and \(\sigma_{s}\) represent the average and standard deviation of all values in the sidelobe area, respectively. In this work, the value of \(psr\) is used to express the contribution of the convolution kernel response map for object tracking. It is used as the basis for the weight of the convolution kernel response map. It should be noted that the final response map of the Siamese network is estimated based on the forward propagation of the network. It is not convenient to directly assign the weights to each convolution kernel response map. The analysis of the computations of the Siamese network shows that directly assigning the obtained weight to the object template feature is equivalent to assigning the weight to each convolution kernel response map. The calculation process of the response map for object matching is expressed as:  
\[f(Z,X)=\varphi(Z)*\varphi(X) \tag{3}\]  
where \(Z\) and \(X\) represent the object image and the search area image, respectively, \(\varphi(Z)\) denotes the convolution feature of the object image, \(\varphi(X)\) denotes the convolution feature of the search area image, and \(*\) represents the correlation operation. Essentially, the correlation operation of the multi-kernel convolution feature is the superposition of the correlation operation of each convolution kernel feature. Assuming that there are \(N\) convolution kernels, then:  
\[f_{l}(Z,X)=\sum_{i=1}^{N}\varphi_{i}(Z)*\varphi_{i}(X) \tag{4}\]