# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 3 Proposed Method
### Siamese Backbone Framework  
Thus far, deep convolutional neural networks have been successfully applied in the field of object tracking. The deepening of these networks has led to improvements in the performance of trackers, such as ResNet [4], ResNeXt [13], and MobileNet [5], which have achieved a good performance. ResNet50, as a classical network, has good robustness and effectiveness and is usually used in trackers as a feature extraction backbone network while modifying the backbone network in order to cater to the accuracy requirements of the tracking task. [PERSON] et al. [20] proposed Flow Alignment FPN (FAFPN) to align feature maps of different resolutions to solve the semantic misalignment problem when fusing features of different layers. We set the steps of the conv4 and conv5 feature layers to 1 and remove the down-sampling operation so that the output resolution of the last three blocks is the same; meanwhile, to increase the receptive field, the use of a dilated convolution [21] to extract more features has been proven to be effective. Transformers [22], as excellent model architectures, are widely used in various vision tasks. Compared to convolutional neural networks, transformers usually use less activation functions and normalization operations with good results. Inspired by this, a similar method is applied in the backbone. The original ResNet50 network uses a convolution of \(7\times 7\) with a 2-step size in the first layer, following a maximum pooling to complete a 4-fold down-sampling of the input image. The transformer divides the image into patches of the same size and feeds each patch into the network. We change the first layer of the network to a convolution of \(4\times 4\) with a 4-step length, with no overlap between convolutions. Compared with the previous one, the convolutional kernel with K = 4 and S = 4 has a smaller kernel size and a larger step size. The computation and parameter numbers are shown in Equations (1) and (2):  
\[FLOPs_{old}:\left(\frac{N}{2}\right)^{2}\times 7^{2}\times 3\times 64=2352N^{2} \tag{1}\]  
\[FLOPs_{new}:\left(\frac{N}{4}\right)^{2}\times 4^{2}\times 3\times 64=192N^{2}, \tag{2}\]  
where \(N\) denotes the input size, and 3 and 64 are the input and output channels in the first layer of the network, leading to a significant reduction in computation. Another difference between transformers and CNNs is the use of activation functions and normalization. RELUs are widely used in various CNN networks as simple and effi  
Figure 1: Illustration of our proposed framework. Section 3.1 presents Siamese backbone network, CNN1, CONV3, CONV4, CONV5 represent layer 3, 4, 5 of it. \(\bigstar\) represents the pixel-level correlation method, which is presented in Section 3.2. The feature fusion model is presented in Section 3.3. The classification and regression sub-network using a dual-attention mechanism, CNN2, is presented in Section 3.4.  
cient activation functions. GELUs, as a variant of RELUs, are used in the latest transformer structures, such as the Swin Transformer and BERT, and can effectively alleviate neuron death and avoid gradient disappearance. Therefore, we use GELUs [23] instead of RELUs. Traditional convolutional neural networks use an activation function after each layer of convolution. In order to speed up the operation, we remove the activation function after the \(3\times 3\) convolution, only using it after the \(1\times 1\) convolution. As for normalization, BN is the most common normalization method, which is widely used in various vision tasks. Meanwhile, the setting of the batch size affects the final result. Models with an insufficient batch size are not suitable for convergence, while there may be a reduction in the generalization ability of models with too large a batch size. Group normalization [24] can be used for the normalization of samples, and it has been used in many application scenarios. We use GN instead of BN and also reduce its use to improve the inference speed.