# Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features
## 5 Conclusions  
In human-computer interactions and other fields that require the use of action recognition methods, it is necessary to recognize actions accurately and quickly to obtain a better experience. In the process of practical experience, we find that the time spans of some actions are different, and there may be an inclusion relationship between action semantics. Based on 2s-AGCN, in this paper we have proposed a skeleton action recognition algorithm based on multi-scale depth spatiotemporal features. The algorithm adopts a multi-scale time sampling module and a depth spatiotemporal feature extraction module, which enriches the receptive field of the feature map and strengthens the extraction of spatiotemporal-related feature information by the network. The experimental data obtained from public datasets showed that the accuracy of the algorithm can be improved. At the same time, these results also give us new inspiration. In the field of human motion recognition based on the skeleton, we should pay full attention to the information contained in the human skeleton, which is likely to have a significant impact on the results of motion \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Date** & **Cross-View (\%)** & **Cross-Subject (\%)** \\ \hline Deep LSTM [32] & 2016 & 67.3 & 60.7 \\ Temporal ConvNet [33] & 2017 & 83.1 & 74.3 \\ VA-LSTM [35] & 2017 & 87.6 & 79.4 \\ Two-stream CNN [36] & 2017 & 89.3 & 83.2 \\ CCA-LSTM [37] & 2017 & 82.8 & 74.4 \\ ARRN-LATM [38] & 2019 & 89.6 & 81.8 \\ MANs [39] & 2018 & 93.2 & 83.0 \\ ST-GCN [5] & 2018 & 88.3 & 81.5 \\ DPRL + GCNN [40] & 2018 & 81.5 & 83.5 \\
2S-AGCN [6] & 2019 & 95.1 & 88.5 \\ RA-GCN [41] & 2020 & 93.6 & 87.3 \\ MV-IGNet [42] & 2020 & 96.3 & 89.2 \\ \hline MST-AGCN (ours) & - & 95.5 & 89.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of accuracy between SOTA methods using the NTU-RGB+D dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Action Type** & **Date** & **Top-1(\%)** & **Top-5(\%)** \\ \hline Feature Encoding [31] & 2015 & 14.9 & 25.8 \\ Deep LSTM [32] & 2016 & 16.4 & 35.3 \\ Temporal ConvNet [33] & 2017 & 20.3 & 40.0 \\ ST-GCN [5] & 2018 & 30.7 & 52.8 \\
2S-AGCN [6] & 2019 & 36.1 & 58.7 \\ GCN-NAS [30] & 2020 & 37.1 & 60.0 \\
1s-AAGCN [34] & 2020 & 36.0 & 58.4 \\
2s-AAGCN [34] & 2020 & 37.4 & 60.4 \\
4s-AAGCN [34] & 2020 & 37.8 & 61.0 \\ \hline MST-AGCN (ours) & - & 37.1 & 61.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of accuracy between SOTA methods using the Kinetics dataset. recognition. Of course, it is not enough to extract only the features in the data. We also need to design a model that is more suitable for the task of action recognition. In future work, our focus will also be on the reconstruction of the model. We are ready to add some modules based on the attention mechanism to better solve the problem of fine-grained actions and add some visual information to solve the problem caused by a single actions. Conceptualization, K.H. and Y.D. ; methodology, K.H. ; software, Y.D. ; validation, Y.D. ; formal analysis, Y.D. ; investigation, Y.D. and J.J.; resources, K.H. and Y.D. ; data curation, K.H. ; writing--original draft preparation, Y.D., M.X. and L.W. ; writing--review and editing, Y.D. and L.W. ; visualization, Y.D. and J.J.; supervision, K.H. ; project administration, K.H. ; funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript. Research in this article is supported by the key special project of the National Key Research and Development Program of China(2018 YFC1405703), and the financial support of Jiangsu Austin Optronics Technology Co., Ltd. is deeply appreciated. Ethical review and approval were waived for this study due to the data being provided publicly. Not applicable. The data and code used to support the findings of this study are available from the corresponding author upon request ([EMAIL_ADDRESS]).