# Auxiliary Task Graph Convolution Network: A Skeleton-Based Action Recognition for Practical Use
## 5 Conclusions and Future Works  
This study presents an auxiliary task graph convolution network (AT-GCN) of skeleton-based action recognition for practical use. AT-GCN is a multi-stream model that uses multiple pathways. Each pathway has a different frame rate to make a variety of features. The proposed method also uses auxiliary tasks to learn a variety of features. Each frame rate feature is converted to each loss. This additional loss leads to a more understandable variety of features for model training. AT-GCN shows higher performance while maintaining the inference time of traditional GCN. In addition, the static sampling method shows higher performance in the static range validation than the conventional training method. The T ensemble uses highly performed models for the length of actions. Proposed methods can be ensembled without additional modules using only sample range differences. T ensembles show similar or higher performance gains than traditional ensemble methods when applied to AT-GCN. Proposed methods were trained and tested with a video that included human action, and no human joints were occluded in the video. However, when considering practical videos from a CCTV, the human joints may sometimes be occluded. These problems can lead to performance degradation because the joint is an important resource for action recognition. The proposed methods divide each pathway by frame rate. However, the changed action sequence also had a deep relationship with action speed. For example, \"looking at the phone\" had a long duration of action, but \"throwing\" had a short duration of action. Future works will include different pathway structures to prevent performance degradation from an insufficient number of joints. Also, future works may include dividing action type by action speed and using this feature to further support the multi-streaming action recognition model. Conceptualization, J.C. and J.-M.P; methodology, J.C.; writing--review and editing, J.C. and S.K. ; supervision, S.K., C.-M.O. and J.-M.P; project administration, S.K. and C.-M.O. ; funding acquisition, S.K. and C.-M.O. All authors have read and agreed to the published version of the manuscript. This work was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) grant, funded by the Korean government (MSIT) (No. IITP-RS-2022-II22103), as part of the Regional Strategic Industry Convergence Security Core Talent Training Program (50%). It was also partially supported by the Innovative Human Resource Development for Local Intellectualization Program through the IITP grant funded by the Korean government (MSIT) (IITP-2024-RS-2022-00156287). Additionally, support was provided in part by the IITP under the Artificial Intelligence Convergence Innovation Human Resources Development Program (IITP-2023 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **X-Sub (\%)** & **X-Set (\%)** & **Time (ms)** \\ \hline ST-GCN [4] & 82.2 & 83.0 & 16 \\ ST-GCN w/AT w/D & **82.7** & **83.2** & 16 \\ \hline MS-G3D [36] & 83.9 & 85.4 & 59 \\ MS-G3D w/AT w/D & **84.2** & **85.7** & 60 \\ \hline CTR-GCN [5] & 83.9 & 84.7 & 48 \\ CTR-GCN w/AT w/D & **84.2** & **85.3** & 72 \\ \hline InfoGCN [32] & 86.0 & 87.1 & 30 \\ InfoGCN w/AT w/D & **86.5** & **87.6** & 30 \\ \hline HD-GCN [33] & 83.8 & 84.9 & 54 \\ HD-GCN w/AT w/D & **84.3** & **85.5** & 54 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of inference time and performance with original and AT-GCN. Time is the inference time. AT indicates the auxiliary task module. D indicates different sample ranges by pathway method. X-Sub and X-Set are the top-1 accuracy. Bold is indicates best performance of each GCN model. RS-2023-00256629), funded by the Korean government (MSIT). It was also partially supported by the Ministry of SMEs and Startups (RS-2024-00435114). **Institutional Review Board Statement**: Not applicable. **Informed Consent Statement**: Not applicable.