# Action Recognition Based on the Fusion of Graph Convolutional Networks with High Order Features
## 4 Experiments
### Ablation Experiment  
In Section 3, we add the joints feature, bones feature, joint-velocity feature, bone-velocity feature, and relative distance feature for action recognition. Since the acceleration feature is similar to the velocity feature, the accuracy after fusion is not significantly improved. The ablation studies of different features are shown in Tables 1 and 2, where J, B, JV, BV, and RD denote that features of joint, bone, joint-velocity, bone-velocity and relative-distance, respectively. Obviously, the multi-feature fusion method outperforms the single-feature-based methods on two benchmark evaluations.  
Tables 3 and 4 are the results on NTU-RGBD-120 dataset. The results also illustrate that the multi-feature fusion method is more effective. The recognition accuracy of our model in NTU-RGBD-120 is slightly lower than the accuracy of NTU-RGBD. The major reasons leading to this result were: (1) NTU-RGBD-120 adds some fine-grained object-related individual actions. For these actions, the body movements are not significant, and the sizes of the objects involved are relatively small; e.g., when \"counting money\" and \"playing magic cube\". (2) Some fine-grained hand /finger motions are added in NTU-RGBD-120. Most of the actions in the NTU-RGBD dataset have significant body and hand motions, while the NTU-RGBD-120 dataset contains some actions that have fine-grained hand and finger motions, such as \"making an ok sign\" and \"snapping fingers\". (3) The third limitation is the large number of action categories. When only a small set of classes is available, each can be very distinguishable by finding a simple motion pattern or even by the appearance of an interacted object. However, when the number of classes increases, similar motion patterns and interacted objects will be shared among different classes, which makes the action recognition much more challenging.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Accuracy (\%) (No Augmentation)** & **Accuracy (\%) (Augmentation)** \\ \hline Joint & 86.7 & 87.7 \\ Bone & 87.0 & 88.1 \\ Joint-Velocity & 86.1 & 86.8 \\ Bone-Velocity & 85.4 & 86.9 \\ Relative-Distance & 87.1 & 87.5 \\ \hline J+B+JV+BV+RD & **90.5** & **91.7** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of the validation accuracy with different input modalities on a cross-subject benchmark of the NTU-RGBD dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Accuracy (\%) (No Augmentation)** & **Accuracy (\%) (Augmentation)** \\ \hline Joint & 93.0 & 93.8 \\ Bone & 93.4 & 94.3 \\ Joint-Velocity & 93.0 & 93.5 \\ Bone-Velocity & 92.7 & 93.4 \\ Relative-Distance & 93.2 & 94.0 \\ \hline J+B+JV+BV+RD & **95.8** & **96.8** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of the validation accuracy with different input modalities on a cross-view benchmark of the NTU-RGBD dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy (\%) (No Augmentation)** \\ \hline Joint & 80.7 \\ Bone & 81.2 \\ Joint-Velocity & 78.5 \\ Bone-Velocity & 79.2 \\ Relative-Distance & 81.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of the validation accuracy with different input modalities on cross-subject benchmark of NTU-RGBD-120 dataset.