# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 4 Conclusions  
The video behaviourrecognition method proposed in this study significantly improves the accuracy and computational efficiency of video behaviour recognition by combining pyramid pooling with an improved two-stream C3D network. Experimental validation shows that the method has good performance on multiple video datasets, especially in reducing computational complexity while maintaining high recognition accuracy. In the future, the network structure can be further optimised, and more feature fusion strategies can be explored to cope with more complex behaviourrecognition tasks. Meanwhile, the model combining optical flow features and spatio-temporal information has a promising application in dynamic video recognition, which is expected to promote further development in the field of video analytics.  
Conceptualization, Y.T. and H.L.; methodology, Y.T.; software, Y.T.; validation, Y.T., X.F. and H.L.; formal analysis, Y.T.; investigation, Y.T.; resources, Y.T.; data curation, Y.T.; writing--original draft preparation, Y.T.; writing--review and editing, Y.T., X.F. and H.L.; visualization, Y.T.; supervision, H.L.; project administration, H.L.; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 61962047), the Inner Mongolia Autonomous Region Science and Technology Major Special Project (grant number 2021 ZD0005), the Inner Mongolia Autonomous Region Natural Science Foundation (grant number 2024 MS06002), the Inner Mongolia Autonomous Region Universities and Colleges Innovative Research Team Program (grant number NMGIRT2313), the Basic Research Business Fund for Inner Mongolia Autonomous Region Directly Affiliated Universities (grant number BR22-14-05), and the Collaborative Innovation Projects between Universities and Institutions in Hohhot (grant numbers XTCX2023-20, XTCX2023-24).  
Not applicable.  
The data presented in this study are available in UCF10, HMDB51, and Hollywood2 datasets, which are publicly accessible. These data were derived from the following resources available in the public domain: UCF10: URL: UCF101 Dataset; HMDB51: URL: [[http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/](http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/)]([http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/](http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/)) (accessed on 4 March 2025); Hollywood2: URL: [[http://www.di.ens.fr/~laptev/actions/hollywood2/](http://www.di.ens.fr/~laptev/actions/hollywood2/)]([http://www.di.ens.fr/~laptev/actions/hollywood2/](http://www.di.ens.fr/~laptev/actions/hollywood2/)) (accessed on 4 March 2025).  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **UCF101 Top-1** & **HMDB51 Top-1** & **Hollywood2** & **Something-Someing V1** \\  & **Accuracy (\%)** & **Accuracy (\%)** & **mAP (\%)** & **Top-1 Accuracy (\%)** \\ \hline C3D & 75.8 & 46.8 & 59.7 & 32.1 \\ Dual-Stream Late Fusion & 81.2 & 50.1 & 63.4 & 35.4 \\ Dual-Stream Late Fusion & 83.7 & 52.5 & 65.9 & 37.2 \\ Pyramid pool + C3D & 85.9 & 55.3 & 68.2 & 40.8 \\ Dual-Stream C3D Early & 87.1 & 56.4 & 70.5 & 42.3 \\ Dual-Stream C3D Late & 88.3 & 57.8 & 71.8 & 43.6 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Method comparison results.