# Human Action Recognition Based on Improved Two-Stream Convolution Network
## 4 Experiments
### Results of Experiments and Analysis
#### 4.3.3 Experimental Overall Analysis  
Table 2 shows the average and the best accuracy (both top1 accuracy) of the four groups of subjects on the UCF101 data set and HMDB51 data set in ten independent repeated experiments. It can be seen from Table 2 that both the BiGRU network and SimAM attention mechanism can effectively improve the action recognition accuracy of the two-stream convolution network. The combination of the two can greatly improve the accuracy of action recognition. As shown in Figure 10, the fusion network accuracy of ten independent repeated experiments is summarized. Figure 10 shows the accuracy distribution of the neural network proposed in this paper in ten repeated experiments on two data sets. The box graph is the summary of the accuracy of each experimental object, and the broken line graph represents the average accuracy of each experimental object. Figure 10 shows that, from the upper edge value, the action recognition accuracy of the neural network (BS-2 SCN) proposed in this paper is the highest. It is worth noting that in Figure 10a, the highest accuracy of the 2 SCN network is equivalent to the average accuracy of the S-2 SCN network and B-2 SCN network, but this accuracy is represented by outliers and is not referential. From the perspective of interquartile distance (IQR), the interquartile distance of the 2 SCN network is the largest, which shows that the accuracy distribution of the 2 SCN network is relatively discrete. On UCF101 data set, the interquartile distance of the S-2 SCN network is the smallest. On the HMDB51 data set, the interquartile distance of the B-2 SCN network is the smallest. In addition, the red broken line diagram in Figure 10 shows the average accuracy of each neural network. By comparing the mean and median of each neural network (the purple horizontal line in the box), we can find that the two values \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Set** & **Network** & **Accuracy** & \multicolumn{4}{c}{**Model**} \\  & & **2 SCN** & **S-2 SCN** & **B-2 SCN** & **BS-2 SCN** \\ \hline \multirow{4}{*}{UCF101} & Spatial & Average & 78.75 & 78.90 & 78.70 & **80.52** \\  & & Best & 78.80 & 79.34 & 78.70 & **81.47** \\  & Motion & Average & 78.99 & 78.18 & 78.33 & **80.00** \\  & & Best & 79.83 & 79.67 & 78.51 & **80.21** \\  & Fusion & Average & 87.47 & 88.83 & 88.45 & **90.07** \\  & & Best & 88.52 & 88.95 & 88.68 & **90.32** \\ \hline \multirow{4}{*}{HMDB51} & Spatial & Average & 78.75 & 78.90 & 78.70 & **80.52** \\  & & Best & 78.80 & 79.34 & 78.70 & **81.47** \\ \cline{1-1}  & Motion & Average & 78.99 & 78.18 & 78.33 & **80.00** \\ \cline{1-1}  & & Best & 79.83 & 79.67 & 78.51 & **80.21** \\ \cline{1-1}  & Fusion & Average & 87.47 & 88.83 & 88.45 & **90.07** \\ \cline{1-1} \cline{2-6}  & Best & 88.52 & 88.95 & 88.68 & **90.32** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of action recognition accuracy (%). Figure 10: Accuracy distributions and means for ten independent repeated experiments of four groups of subjects on two data sets (**a**) UCF101. (**b**) HMDB51. are relatively close, which shows that in many experiments, the accuracy distribution of each neural network is relatively uniform and the degree of dispersion is low. To analyze the stability of the action recognition accuracy of each neural network in multiple experiments, we compared the accuracy variance of each neural network in ten independent repeated experiments, and the results are shown in Figure 11.  
Figure 11 shows the accuracy variance of ten independent repeated experiments of each neural network on the UCF101 data set. We can see that the variance of the 2 SCN network is much larger than that of the other three networks, while the variance of the S-2 SCN network is the smallest. However, as shown in Figure 11, the variance of the B-2 SCN network is the smallest, while the variance of the 2 SCN network is still much larger than that of the other three networks.