# Tracking and Registration Technology Based on Panoramic Cameras
## 3 Method
### Panoramic Depth Estimation Sub-Network
The shallow features obtained after the first downsampling are also passed to the decoder via skip connections, further enhancing the decoder's ability to interpret features. However, with only two downsampling stages, the network struggles to capture deep, high-level features from the input image. To compensate for this, a dilated convolution module is introduced to enlarge the receptive field and better extract global contextual information from the image. Furthermore, to enhance the representational capacity of features, the dilated convolution is combined with skip connections, and \(1\times 1\) convolution filters (D3 and C7) are used to fuse shallow and deep features. In addition, all convolution operations are followed by the ELU activation function to improve the network's ability to model nonlinear representations. **Decoder**: The decoder consists of transposed convolution, upsampling, skip connections, and depth prediction operations. The output feature dimensions and corresponding hyperparameters of each operation are listed in Table 2. To match the two downsampling operations performed by the encoder, the decoder utilizes two transposed convolutions for \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Operations** & **Kernel Size** & **Stride** & **Padding** & **Dilation Rate** & **Output Dimensions** \\ \hline Input & & & & & & \(3\times 256\times 512\) \\ C1\_1 & Conv2d & \(3\times 9\) & \(1\) & \(1\times 4\) & \(1\) & \(8\times 256\times 512\) \\ C1\_2 & Conv2d & \(5\times 7\) & \(1\) & \(2\times 3\) & \(1\) & \(8\times 256\times 512\) \\ C1\_3 & Conv2d & \(5\times 11\) & \(1\) & \(2\times 5\) & \(1\) & \(8\times 256\times 512\) \\ C1\_4 & Conv2d & \(7\times 7\) & \(1\) & \(3\times 3\) & \(1\) & \(8\times 256\times 512\) \\  & Concation & & & & & \(32\times 256\times 512\) \\ C2\_1 & Conv2d & \(3\times 9\) & \(1\) & \(1\times 4\) & \(1\) & \(16\times 256\times 512\) \\ C2\_2 & Conv2d & \(3\times 7\) & \(1\) & \(1\times 3\) & \(1\) & \(16\times 256\times 512\) \\ C2\_3 & Conv2d & \(3\times 5\) & \(1\) & \(1\times 2\) & \(1\) & \(16\times 256\times 512\) \\ C2\_4 & Conv2d & \(3\times 5\) & \(1\) & \(2\times 2\) & \(1\) & \(16\times 256\times 512\) \\  & Concation & & & & & \(64\times 256\times 512\) \\ C3 & Conv2d & \(3\times 3\) & \(2\) & \(1\times 1\) & \(1\) & \(128\times 128\times 256\) \\ C4 & Conv2d & \(3\times 3\) & \(1\) & \(1\times 1\) & \(1\) & \(128\times 128\times 256\) \\ C5 & Conv2d & \(3\times 3\) & \(1\) & \(1\times 1\) & \(1\) & \(128\times 128\times 256\) \\ C6 & Conv2d & \(3\times 3\) & \(2\) & \(1\times 1\) & \(1\) & \(256\times 64\times 128\) \\ D1 & Conv2d & \(3\times 3\) & \(1\) & \(2\times 2\) & \(2\) & \(256\times 64\times 128\) \\ D2 & Conv2d & \(3\times 3\) & \(1\) & \(4\times 4\) & \(4\) & \(256\times 64\times 128\) \\  & Concation & & & & & \(512\times 64\times 128\) \\ D3 & Conv2d & \(1\times 1\) & \(1\) & \(4\times 4\) & \(4\) & \(256\times 64\times 128\) \\ D4 & Conv2d & \(3\times 3\) & \(1\) & \(8\times 8\) & \(8\) & \(512\times 64\times 128\) \\ D5 & Conv2d & \(3\times 3\) & \(1\) & \(16\times 16\) & \(16\) & \(512\times 64\times 128\) \\  & Concation & & & & & \(1024\times 64\times 128\) \\ C7 & Conv2d & \(1\times 1\) & \(1\) & \(0\times 0\) & \(1\) & \(512\times 64\times 128\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Output shapes and hyperparameters of encoder operations. upsampling. First, a transposed convolution (T1) is applied to upsample the deep features extracted by the encoder. To enhance feature reconstruction, the upsampled features are concatenated with the shallow features of the same spatial resolution passed from the encoder via skip connections. Two large-kernel convolutional layers (C8 and C9) are then applied to the concatenated features to perform depth estimation, generating a coarse depth map at half the input resolution.