# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 3 Experimental Section
### Experiment 2: Behavioural Feature Extraction for Variable-Scale Video
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Modalities** & **Original 3D Convolutional** & **3D Convolutional Neural Networks** \\  & **Neural Network** & **for 3D Pyramid Pooling** \\ \hline Fixed Scale & 77.8\% & 80.2\% \\ (training from scratch) & 82.1\% & 82.4\% \\ Variable scale & & \\ (using pre-trained weights) & unsupported & 83.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effects of variable-scale training and pyramid pooling. effective in extracting spatio-temporal features, while the variable-scale training helps to reduce overfitting. Experiment 3: Video Character Behaviour Classification Experiment Based on Two-Stream Improved C3D Network  
Video behaviour classification experiments based on a dual-stream improved the C3D network on UCF101 and HMDB51 datasets. The HMDB51 dataset contains 51 behaviours, and the data sources include movies, web videos, etc., which are highly challenging. In this experiment, features are trained on RGB stream and optical stream 3D network streams, respectively. In the RGB stream, the input is a sequence of 16 unstacked video frames with the same parameters as the original C3D structure; in the optical flow 3D network, the initial learning rate is set to 0.003, and after 20,000 and 40,000 iterations, the learning rate decreases to one-tenth of the initial value. Thirty data are used for each training. After training, feature fusion of the two-stream network is performed, using early fusion and late fusion, respectively. Early fusion involves L2 normalisation of the output vectors of each C3D network after the FC6 layer of the two streams and then concatenating the vectors of the two streams; late fusion involves averaging the category probabilities after the Softmax output of each stream. In order to evaluate the impact of early fusion and late fusion on behaviour-recognition accuracy, this experiment was conducted on the UCF101 and HMDB51 datasets, and the results are shown in Table 5. The experiments show that early fusion is superior to late fusion, which may be due to the fact that early fusion is able to retain more feature information in the convolutional layer, which can be fused before maximum pooling, and more effectively characterise the movement of people in the video. On the UCF101 dataset, the original 3D convolutional neural network shows a significant improvement in recognition accuracy compared to our proposed two-stream improved C3D network. This may be attributed to the 3D pyramid pooling layer, which can more effectively extract and retain spatio-temporal information feature. Meanwhile, the dual-stream improved C3D network performs better in spatio-temporal feature extraction, especially through the extraction of optical flow features, which further enhances the model's ability to capture the spatio-temporal features of people's movements in the video. Finally, the early fusion of dual-stream features better preserves the features of the respective streams compared to the late fusion, thus improving the recognition accuracy.