# 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  
First, the effectiveness of video feature extraction using the variable-scale behavioural algorithm is tested on the UCF101 dataset, a widely used behavioural recognition dataset containing 101 different behavioural categories [29], with videos from YouTube and other web sources. The dataset is widely used for behaviour recognition, action classification, and video-understanding research. \begin{table}
\begin{tabular}{c c c} \hline  & **UCF101** & **Hollywood2** \\ \hline S:M = 8:2 & 96.5\% & 76.9\% \\ S:M = 6:4 & 95.2\% & 75.3\% \\ S:M = 4:6 & 94.9\% & 74.7\% \\ S:M = 2:8 & 91.4\% & 71.4\% \\ \hline \end{tabular}
\end{table} 
Table 2: Results of the contribution of various dynamic and static features for both datasets. Figure 7: Different categories of motion on Hollywood2 for S:M of 8:2 and 6:4. Two feature extraction methods are used in this experiment for video analysis (Table 3): a regular 3D convolutional neural network is used to extract standard video 3D convolutional features; a pyramid pooling 3D convolutional neural network introduces a pyramid pooling layer, which aims to enhance the feature extraction capability under multi-scale video input, especially when dealing with videos of different scales, and can effectively enhance the recognition accuracy. Two training strategies are also used in the experiments: variable input scale training and fixed input scale training. In variable input size training, the video size is set to different scales (e.g., 161 \(\times\) 112, 162 \(\times\) 220, 320 \(\times\) 112, etc.) [30], the initial learning rate is 0.001 and adjusted to one-tenth of the original learning rate after 15,000 and 20,000 iterations, respectively, and each training batch contains 30 video data. Fixed input size training, on the other hand, unifies the video input size and is initialised using 3D convolutional network weights pre-trained on the Sport-1M dataset, with the same learning rate adjustment strategy as variable input size training. During the experiments, all models are trained on the UCF101 Split 1 dataset, and only the RGB frames of the original video are used as inputs, and finally, the behaviour-recognition accuracy of each model on the dataset is calculated. Experiments were conducted to observe the effect of pyramid pooling levels and parameters on feature extraction, with the aim of investigating whether the pooling level affects the performance of the extracted features in behavioural recognition and how many parameters are needed for training. Different pyramid pooling settings lead to different feature extraction qualities Two types of pyramid pooling are set up in this chapter: the first is a two-level pyramid pooling, and the other is a three-level pyramid pooling, and the pooling level for each level is calculated in the same way as in the introduction. Table 4 shows the experimental results, and it can be concluded that using the first type of pyramid pooling gives slightly higher accuracy than the general 3D convolutional neural network architecture but requires fewer training parameters. Using the second type of pyramid pooling gives the best results but also has a relatively large number of parameters. The results show that the model with a 3D pyramid pooling layer outperforms the standard 3D convolutional neural network. With both training strategies, variable input size training is about 1% more effective than fixed input size training. This result may be attributed to the fact that the pre-trained weights and 3D pyramid pooling layer are more \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Infrastructure** & **Accuracy** & **Parameters (Millions)** \\ \hline Raw 3D convolutional neural network & 82.1\% & 77.9 \\ Pyramid pooling on two levels & 82.7\% & 54.9 \\ Pyramid pooling on three levels & 83.6\% & 88.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of accuracy and number of parameters for different layers of pyramid pooling versus raw 3D.