# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 3 Experimental Section
### Experimental Results and Analyses  
The experiment mainly adopts the Top-1 accuracy rate as the key index for performance evaluation and compares it with traditional methods (e.g., SVM, traditional RNN, etc.). The specific evaluation indexes include the Top-1 accuracy rate, which measures whether the most probable category in the classification result is consistent with the real category and the Top-5 accuracy rate, which evaluates whether the first five predicted categories in the classification result contain the real category. These standard assessment metrics provide the basis for the comparison of the experimental results. From the above experimental results, it can be seen that the GRU + 3D-CNN method proposed in this paper achieves significant performance improvement in both the UCF101 \begin{table}
\begin{tabular}{c c c} \hline \hline  & **UCF101** & **HMDB51** \\ \hline Early integration & 89.23\% & 64.32\% \\ late fusion & 87.34\% & 58.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recognition rates of early fusion and late fusion in both datasets. and HMDB51 datasets (Tables 6 and 7). Compared with the traditional RNN model, GRU reduces the problem of gradient vanishing through a more effective gating mechanism and thus performs better in long time-dependent tasks. And the introduction of GRU helps to better capture the temporal dynamics and further improves the classification accuracy compared to the 3D-CNN approach alone. In addition, the model is also analyzed by confusion matrix and found to have high accuracy for recognition of most action categories, especially in scenes with complex actions and fast movements, where the advantage of the GRU layer is more obvious. In terms of computational efficiency, the training and inference times of the different models were compared. The training time and inference time of the GRU + 3D-CNN model are improved compared to the traditional RNN, but the significant improvement in accuracy makes it a worthy option. The specific training times are shown in Table 8.  
Although GRU + 3D-CNN takes longer to train, this added time is worthwhile compared to the improved recognition accuracy. The experimental results show that the dual-stream C3D network (especially the late fusion) and the pyramid pooling technique perform well on several standard datasets and significantly improve the accuracy of action recognition (Table 9). Especially on the UCF101 dataset, the dual-stream C3D network achieves an accuracy of 88.3%, which is about 5% higher than other methods, proving the effectiveness of the dual-stream network in fusing spatial and temporal features. Combined with pyramid pooling, the accuracy of the C3D network on UCF101 is improved to 85.9%, which enhances the spatial-information-processing capability of the model through multi-scale feature extraction. The experiments also show that dynamic features are crucial for improving the recognition accuracy, and the two-stream C3D network outperforms the traditional 3D-CNN on the UCF101 and HMDB51 datasets. In addition, the late fusion outperforms the early fusion on most of the datasets, and the fusion is significantly better on UCF101 in particular. Pyramid pooling also effectively enhances the multi-scale feature extraction capability of the model when dealing with complex scenes such as Hollywood2 and Something-Something V1. Overall, combining these techniques not only improves the accuracy of video action recognition, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodologies** & **Top-1 Accuracy (\%)** & **Top-5 Accuracy (\%)** \\ \hline traditional RNN & 75.2 & 92.1 \\
3D-CNN & 84.3 & 96.3 \\ GRU + 3D-CNN & 89.6 & 98.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of the UCF101 dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Modelling** & **Times (Hours)** \\ \hline Traditional RNN training time & 10 \\
3D-CNN training time & 12 \\ GRU + 3D-CNN training time & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Timetable.