## 6 High-Level Abstract-Feature Extraction
### Wide ResNet Modification
#### 6.2.3 Adding 3D Convolution Layers and Depth Compaction  
Because the aforementioned network structures directly superimposed all 25 consecutive frames along the u and v channels, the temporal feature fell on the channel axis. However, two-dimensional (2D) convolution slid along the width and height axes and the convolution kernel value on the channel axis was equal to the channel input value. Therefore, the extracted temporal feature was a global feature. In other words, use of a simple 2D convolution layer may have caused the loss of numerous detailed local temporal features. A quick and intuitive method for extracting these features is use of 3D convolution layers. The input format of each 2D convolution layer was batch, height, width, channel, whereas that of each 3D convolution layer was adjusted to batch, depth, height, width, channel. In other words, an independent axis, namely depth, was assigned to time and the 3D convolution kernel, as shown in Figure 7 (e.g., \(3\times 3\times 3\)), and slid along the depth, height, and width axes. Constructing Wide ResNet by using only 3D convolution layers was unfeasible because this approach would use a considerable amount of graphics card memory. Therefore, this study proposed a method of connecting 3D and 2D convolution layers. This method compressed the local temporal features obtained by 3D convolution layers and converted these data into a format that 2D convolution layers could process; finally, the data were processed by Wide ResNet established by 2D convolution layers to obtain optimal results. To retain the local temporal features and input these features into convolution layers, this study computed the mean values of results obtained by 3D convolution layers along the depth dimension (i.e., the time dimension). Subsequently, the original feature map was processed by multiple convolution kernels through 3D convolution computation. This process divided the dense optical flow into multiple sparse high-level feature maps. Subsequently, these sparse local temporal features were compressed to a 2D plane and mapped \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group Name** & **Kernel** & \multicolumn{2}{c}{**Output SIZE**} & **Note** \\  & **Size** & **(Batch,Height,Width,Channel)** & \\ \hline Input & & (128,60,38,50) & \\ Initial Conv2d & 5 & (128,60,38,32) & Stride = 1 \\ Res Group 01 & 5,3 & (128,60,38,16k) & N = 2 \\ Res Group 02 & 3 & (128,30,19,32k) & k = 4 \\ Res Group 03 & 3 & (128,15,19,64k) & Drop rate = 0.3 \\ \multicolumn{4}{c}{Batch Normalization} \\ \multicolumn{4}{c}{Activation function} \\ Global Average Pooling & \([15\times 19]\) & (Batch,1,1,64k) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Modified Wide Resnet structure and kernel size. Figure 7: 3D convolution layers. to the spatial plane. Because of the process of computing the mean value, differences caused by time were eliminated. In other words, the inconsistency in 2D convolution computation resulted from the different starting points of the gait cycle in the sample being effectively eliminated. As shown in Figure 8, compared with locating the time difference on the channel axis and conducting a global operation by using 2D convolution layers, this operation achieved optimal distinction of temporal features and obtained more feature information, which in turn enhanced the representation of the entire network. With respect to the actual network structure, this study inputted the 3D feature map into batch normalization and the activation function before conducting feature compression.