## 7 Experiments
### Comparison with Related Research  
According to Table 10, the dataset employed in studies [5,7] (CASIA) differed from and was much smaller than that employed in the present study, namely the TUM-GAID Gait Dataset. In [5], additional computer-generated data (Human3.6M dataset for foreground and CASIA for background) were employed to increase the data size; however, this takes the model away from reality. In [6], an accelerometer was employed, and in [5, 7, 6], feature vectors related to displacement were employed as input instead of optical flow. These observations can explain the low reference value of these studies for the present study. Therefore, these studies are not discussed further. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sample Frame** & **Loss** & **Accuracy** \\ \hline Training Set & 15 frames & 0.050401 & 98.40\% \\  & 25 frames & 0.002754 & 99.97\% \\  & 30 frames & 0.000066 & 100\% \\ Testing Set & 15 frames & 0.893275 & 78.35\% \\  & 25 frames & 0.484642 & 88.06\% \\  & 30 frames & 0.341710 & 92.26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Effects of sample frame number on accuracy. In the method employed in [9], sampling was set at 25 frames per unit. The samples of half of the pedestrians were used for training and those of the other half were tested using transfer learning to verify the effectiveness of the NN in terms of processing gait optical flow. However, this study employed a VGG-like network that contained two fully connected layers and a high number of parameters. Therefore, in addition to a considerable model size, the computational demand was higher than that of the ResNet-based NN model used in the present study. The requirement for equipment was high ([9] used Tesla K40c for experimentation). In addition, the processing method employed in [9] did not conduct tracking on pedestrians in addition to capturing optical flow. By contrast, the present study took the middle segment of the pedestrian image sequences to present the target pedestrian completely in the center of the image and reduced the image size to one-eighth of the original size. However, the method employed in the present study applied automatic pedestrian detection, which focused on the pedestrian, to reduce the number of parameters in the model with a smaller input. In addition, this method had slightly higher accuracy and its data were easier to transfer to other datasets for computation. In [8], Wide ResNet (N = 3, k = 4), which conducted pedestrian detection and pedestrian segmentation, was employed. This method segmented a human body into five parts, namely the entire body, left foot, right foot, upper body, and lower body, and reduced the image size of each part to 48 \(\times\) 48. Feature vectors extracted from all five parts were then incorporated for NN training. The pedestrian detection method employed in [8] was a simple background-filtering method that was less friendly with data that were unable to provide information of background areas without pedestrians. By contrast, the present study employed YOLOv2 for pedestrian detection. This method was not concerned with the background and extracted the ROI in a rectangular shape fit for the body form of a pedestrian, and thus was capable of filtering excessive background information. Additionally, the preprocessing measure in [8] was more complex than that in the present study because an entire sequence was employed for each sample (the maximum was 90 frames; employment of only 50 frames reduced the accuracy rate to 94.28%). In addition, the optical flow employed was an RGB image (three channels) and the total input size was 48 \(\times\) 48 \(\times\) 270. By contrast, the input size of the present study was 60 \(\times\) 38 \(\times\) 2 \(\times\) 25. Therefore, the number of parameters was reduced by more than 1 million. The sampling method was the stepping sliding method.