# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 3 Experimental Section  
The experiments were performed on a workstation with an NVIDIA GTX 1080 Ti graphics card using the TensorFlow framework. The training parameters were set to a learning rate of 0.001 and a batch size of 32; the optimiser was selected as Adam, and the loss function was cross-entropy. The model training lasted for 10 epochs, and an early-stop strategy was introduced to prevent overfitting, where training is automatically terminated when the validation set loss stops decreasing.  
Experiment 1: Experimental Study of Video Behaviour Recognition with Different Feature-Contribution Ratios  
The experiments in this section aim to achieve the optimal combination of static and dynamic features through optimal feature fusion to improve the accuracy of video behaviour recognition. Considering the different effects of different datasets on the feature-contribution ratio, different dynamic and static feature-contribution ratios are used in the experiments. Specifically, we set up four groups of different dynamic and static feature-contribution ratios, as shown in Table 1. By combining the second column of the \(\rho\)1 or \(\rho\)2 relationship with \(\rho_{1}\)=\(\sqrt{1-\rho_{2}^{2}}\)[27], by adjusting these ratios, we are able to evaluate their effects on recognition under different feature combinations, thus verifying the effectiveness of feature fusion.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dynamic and Static Ratios** & **Relationship Between P\({}_{1}\) and P\({}_{2}\)** & **Eigenvector (Math.)** \\ \hline
20\%S, 80\%M & p1 = 4p2 & C = \(\frac{4}{\sqrt{17}}\)M + \(\frac{1}{\sqrt{17}}\)S \\
40\%S, 60\%M & 2p1 = 3p2 & C = \(\frac{3}{\sqrt{13}}\)M + \(\frac{3}{\sqrt{13}}\)S \\
60\%S, 40\%M & 3p1 = 2p2 & C = \(\frac{2}{\sqrt{13}}\)M + \(\frac{3}{\sqrt{13}}\)S \\
80\%S, 20\%M & 4p1 = P2 & C = \(\frac{1}{\sqrt{17}}\)M + \(\frac{4}{\sqrt{17}}\)S \\ \hline \end{tabular}
\end{table}
Table 1: Eigenvector expressions for different dynamic and static feature contributions.  
Figure 6: Structure of dual-stream improved C3D network.  
After determining the contribution of different motion features, this experiment will be conducted on the UCF101 and Hollywood2 datasets. The Hollywood2 dataset contains 10 scene types and 12 character motion types, with a total of 3669 video clips of 20.1 h, all from 69 Hollywood films [28], thus making it more challenging. Table 2 shows the results of the feature fusion experiments for the UCF101 and Hollywood2 datasets based on different dynamic and static feature contributions.  
From the results, it can be seen that a ratio of 8:2 performs best for recognition accuracy for both datasets. However, the best dynamic and static feature-contribution ratio of 6:4 for certain action categories, such as standing, sitting up, etc., suggests that certain actions rely on more dynamic features, which affects the final classification results.  
Figure 7 demonstrates the performance of different action categories in the Hollywood2 dataset when S:M is 8:2 and 6:4. Whether S:M = 8:2 or S:M = 6:4, the contribution of static features accounts for a relatively large proportion, especially in the later stage, where the fusion of GRU features effectively compensates for the timeline features ignored by the CNN network in single-frame image recognition.