## 5 Experiment and Evaluation
### Experimental Results  
Our evaluation results of the above methods are shown in Table 4. The first two rows are RGB video-based baseline methods I3D and TSN. Row 3 is depth maps-based baseline method HON4D. The following rows report the performances of ST-GCN, 2s-AGCN, and the proposed feature-enhanced LSTM-DGCN model. Our model learning model outperformed other methods in all the evaluation settings. The first two lines show the evaluated I3D and TSN, which are action recognition methods based on RGB video. Compared with other methods, the accuracies of these methods were slightly lower because the characteristics of a basketball players' actions cannot be well represented in a 2D image. The evaluation result of HON4D for the cross-view is better than for the cross-subject view. The reason for this difference is that in the cross-view scene, the habits of the same action are almost the same, and methods based on the depth map are easier to use for learning the appearance- or subject-related action mode. We can see that the results of our method are much better than those of other classical action recognition methods on our dataset. On the one hand, the original experimental datasets for other methods are relatively simple, not having the complex environmental conditions and action set such as our NPU basketball players' action dataset. On the other hand, the method for a video frame and depth map with serious background noise interference is difficult to use for guaranteeing robustness. In addition, compared with the traditional skeleton-based motion recognition methods, our method pays more attention to the features of critical joint angles and joint distances and takes the human skeleton structure and action timing relationship into account. It is not sensitive to the discontinuity caused by the missing frame data as well, which reduces the data requirement. We compared the two methods of human skeleton data processing using GCN: ST-GCN and 2s-AGCN. The former is a classic baseline of such methods, which is often used for comparison, whereas the latter is the most advanced model of such methods so far and has achieved the best results. We chose to compare these two methods to prove the effectiveness and excellence of our method. To set up the running environment of ST-GCN and 2s-AGCN, we first extracted the coordinates of key points from NPU RGB+D bone data to generate a skeleton file by imitating the format of an NTU RGB+D dataset, and then analyzed them to get the files with npy as the suffix of a cross-subject, cross-view, and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Cross Validation Accuracy (\%)** & \
\begin{tabular}{c} **Cross-Subject Evaluation** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Cross-View Evaluation** \\ **Accuracy (\%)** \\ \end{tabular} \\ \hline I3D [27] & 12.50 & 16.67 & 25.00 \\ \hline TSN [28] & 14.58 & 20.83 & 35.41 \\ \hline HON4D [29] & 28.71 & 30.35 & 41.02 \\ \hline ST-GCN [6] & 70.40 & 72.40 & 72.80 \\ \hline
2s-AGCN [20] & 71.80 & 74.80 & 78.16 \\ \hline Feature-enhanced & \multirow{2}{*}{80.90} & \multirow{2}{*}{81.60} & \multirow{2}{*}{83.70} \\ LSTM-DGCN & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of the three evaluation settings of our benchmark using different methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Method** & **Train** & **Test** \\ \hline Cross-Subject Evaluation & 1585 (73\%) & 584 (27\%) \\ \hline Cross-View Evaluation & 1767 (81\%) & 402 (19\%) \\ \hline Cross Validation & 1517 (70\%) & 651 (30\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data division under three evaluation standards. cross validation input of the model. Then, we added the parameter configuration file of the NPU RGB+D dataset to set the parameters of the feeder and model. Finally, we ran the models and obtain the evaluation results under different data partitions.