## 3 Convolution Kernel Feature Analysis
In order to further analyze the contribution of different convolution kernels in object tracking tasks, we select two typical video sequences \"soccer\" and \"jump\" from the OTB benchmark dataset and analyze the matching response of the object in each convolution kernel. The response maps of each convolution kernel in the \"soccer\" video sequence are shown in Figure 3.  
The leftmost image in Figure 3 presents the object tracking result, and the second image on the left denotes the matching response map of the object template and the search area. It is evident that the quality of the object response map is relatively good, and there is a significant peak at the real position of the object. However, it should be noted that the final response map is the result of the common matching of all convolution kernel features in the object convolution feature. If each convolution kernel feature of the object image is extracted separately and the matching response is performed, a response map of the object in each convolution kernel is obtained as shown in the figure. The figure also shows that some convolution kernels, such as 1-17, 20, 22, 26, and 30, have significant peaks in the object area (in this case, the central area). The overall distribution is similar to a two-dimensional Gaussian distribution, i.e., the convolution kernel in this part makes the primary contribution to the final object response map. At the same time, the response graphs of other convolution kernels, such as 18, 19, 21, 23, 24, 25, 27, 28, 29, 31, and 32, either have no significant peaks or have peaks at wrong positions. These convolution kernel features do not contribute to the final response map and may degrade its quality. Similar to the \"soccer\" video sequence, the response maps of the \"jump\" video sequence for each convolution kernel are shown in Figure 4.  
Figure 3: The response maps of different convolution kernels in response to the object’s position (“soccer” video sequence). The response maps of the convolution kernel for the \"jump\" video sequence are similar to that of the \"soccer\" video sequence. The leftmost image in the figure denotes the result of object tracking, the second image on the left denotes the object matching response map, and the image on the right side of the figure represents the independent response map of each convolution kernel. As presented in the figure, for the \"jump\" video sequence, the response quality of each convolution kernel is also quite different. Among them, 2-17, 26, and 30 have better response quality and those significant peaks are in the center of the object. On the other hand, the response quality of other convolution kernels is poor. It should be noted that the convolution kernel with a good response for the \"jump\" video sequence is different from the \"soccer\" video sequence, which means that for different objects, the convolution kernels that contribute to the object response map are different. Based on the aforementioned analysis, it can be concluded that for different tracking objects and in different tracking environments, the response levels of different convolution kernels in a convolutional layer for the same object are inconsistent. Consequently, their contribution to the tracking task is also inconsistent. Therefore, it is impossible to adapt to all objects by modifying the convolution structure of the Siamese convolutional neural network at one time. In order to achieve stable object tracking, it is necessary that the object tracking method adaptively selects different convolution kernels for different objects, makes full use of the useful features in convolution features, suppresses inefficient features, and achieves stable matching of object features. Based on the aforementioned analysis regarding the characteristics of the convolution kernel, we propose an object-aware adaptive convolution kernel attention module.