# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 4 Experiment
### Comparison of State-of-the-Art Methods  
Florence-3D: The proposed method achieves a recognition accuracy of 96.59%, taking the lead for the performance competition given in Table 5. In [34], the authors directly employed original joint coordinates as spatial features and then used Bag of Words approach to encode features. In [35], [PERSON] et al. extracted geometric features from 3D skeleton data to model skeleton human action information in the group space, and a CNN model was used for learning and classification. In [36], the authors considered each body joint as a kinematics sensor to calculate the Linear Joint Position Feature (LJPF) and Angular Joint Position Feature (AJPF) to encode complex human actions. Both of the above approaches propose rich geometric features to characterize actions, but they ignore the importance of the temporal dimension information.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Normalization Function** & **Florence-3D** & **UTkinect-3D** \\ \hline Log 0 & 85.74\% & 89.94\% \\ Maxmin 0 & 96.59\% & 97.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compare the recognition results of two normalization function on two datasets using our network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Feature Combination** & **Accuracy** \\ \hline \multirow{2}{*}{Florence-3D} & original coord + disp + orien & 93.64\% \\  & relative coord + disp + orien & 96.59\% \\ \hline \multirow{2}{*}{UTkinect-3D} & original coord + disp + orien & 93.13\% \\  & relative coord + disp + orien & 97.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compare the recognition results of original coordinate and relative coordinate combine with other two features separately on two datasets using our network (coord represents coordinate of joints and disp, orien denote the immediate displacement and immediate motion orientation).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy** \\ \hline Lie group + CNN [35] & 93\% \\ Skeletal BoW [34] & 94.34\% \\ Learning feature combonation [37] & 94.39\% \\ HDS-SP [38] & 95.88\% \\ Kinematics posture feature [36] & 96\% \\ Our method & 96.59\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Compared with the state-of-the-art methods on Florence-3D dataset.  
UTkinect-3D: The overall recognition accuracy results on UTkinect-3D are presented in Table 6. It can be seen that our proposed method achieves the second-best performance. In [39], [PERSON] et al. input original joints and several other geometric features into a multistream LSTM for recognition, after fusing the score to obtain the final result. In [40], a skeleton-based graph structure was applied to represent human motion data. Nevertheless, the topology of the graph is fixed over all layers, only local features can be obtained and it lacks collaboration information for long distances. In [41], the authors proposed a new action recognition scheme to describe human motion information more accurately and robustly. The scheme concatenated relative joint position (RJP), joint velocity and joint acceleration to form a spatial-temporal representation. Dynamic time warping (DTW) and Fourier temporal pyramid (FTP) are employed to handle the rate variations and noise in the skeleton data, respectively.