## 3 Convolution Kernel Feature Analysis  
Usually, the convolutional neural networks have multiple convolutional layers, and each convolutional layer has multiple convolution kernels. Therefore, analyzing the response of each convolutional layer and convolution kernel can provide useful information for improving the object tracking method. First, in this work, we regard each frame in the video as an independent static image. After the image is passed through the convolutional neural network, a series of convolutional feature maps are formed. Based on the different levels of convolution feature extraction, those features are divided into low-level convolution features, middle-level convolution features, and high-level convolution features. In other words, the feature maps of different levels denote different levels of information extracted from the input image. For simplicity, in this work, we choose AlexNet in the SiameseFC tracking method to extract the convolutional features of each convolutional layer from the image. Then, we obtain the response of the object based on cross-matching, as shown in Figure 1. Since there are many channels in the high-level convolution features, only some typical features are extracted in order to highlight the key points. Figure 1: The response of different convolutional layers to the object in the input image. [PERSON] et al. [18] show that the high-level features (Conv5) mainly reflect the semantic features of the input image. By matching the semantic features, the object to be tracked can be distinguished from the background area. However, since the receptive field of a single pixel in high-level convolution features is relatively large, relying only on high-level convolution features in object tracking may provide an inaccurate position, which may reduce the tracking accuracy while ensuring robustness. At the same time, the low-level features (Conv2) retain more detailed information describing the instance level information, such as the boundary and texture of the object. [PERSON] et al. [18], [PERSON] et al. [17], etc. have proposed to make full use of multi-layer features in order to improve the object features and eventually object tracking performance. Based on the feature analysis of the aforementioned convolutional layers, in this work, we further explore the role of different convolution kernels in each layer of convolutional features for specific tracking tasks. The basic assumptions of this work are as follows. * In the object tracking method, the objects, and scenes to be tracked are diverse. * In different tracking scenarios, the convolution kernel activated by the object in a convolution layer may be different. * During the tracking process, if all the convolution kernel features are used for feature matching, then the pertinence of the object is weakened, resulting in a lack of distinct characteristics of the object. In order to verify these hypotheses, we use the \"blurcar1\", \"coke\", \"david\", \"soccer\", and \"toy\" video sequences in the benchmark set to perform experiments. The response of each object for different convolution kernels in the Conv5 convolution layer is shown in Figure 2.  
As shown in Figure 2, the objects and environments in different video sequences perform differently for each convolution kernel in the Conv5 convolutional layer. The response position of the \"blurcar1\" sequence in the third convolution kernel matches  
Figure 2: The response of different convolutions towards the object in the Conv5 convolution layer. the position of the object contained in the image. The \"coke\", \"david\", \"soccer\", and \"toy\" video sequences have better activation effects in the 113 th, 124 th, 171 st, and 235 th convolution kernels of Conv5 layer, respectively. It is evident from Figure 2 that the response maps of other convolution kernels are sparse and contain a lot of noise. Therefore, they are unable to reflect the location of the object in the image.