## 6 Discussion  
The NPU RGB+D dataset proposed in this study is a single-player basketball video shot at an indoor basketball court. At present, it includes 12 kinds of basketball player actions, involving dribbling, passing, and catching, and shooting. It contains the basic atomic basketball actions selected based on the FIBA basketball rules and the technical guidance of professional basketball coaches. It has an important foundational significance for the recognition of all complex basketball actions. It not only has important value in the research on basketball action recognition but also has high application value in the intelligent judgment of basketball games and daily training of basketball players. However, the dataset does not involve the actions of multiple basketball players in a basketball game. First, multiple depth cameras are needed to cover the whole field of view of the basketball court. Second, multiple basketball players should be tracked. Third, basketball players must be matched with different camera fields of vision through triangulation positioning to determine their positions on the court and solve the problem of mutual occlusion. The information of each player on each timestamp is separated and written into the dataset as valid data.  
When extracting skeleton coordinates from RGB+D video taken by ZED camera, the skeleton data may be incomplete and inaccurate owing to overly long distances and insufficient illumination. Therefore, we should not only improve the accuracy of the 3D human key point coordinate extraction algorithm based on ZED and OpenPose, but also improve the robustness of the LSTM-DGCN algorithm to deal with noise and missing data.  
Our NPU RGB+D dataset, as the only existing large RGB+D dataset for basketball action recognition, has rich and diverse action data, which can provide support and a benchmark for the further research on basketball action recognition, especially for the depth-based action recognition method. The experimental results show that large-scale and effective available data are very helpful for the training of a data-driven deep learning model. A part of the shooting data can even be selected to train a scoring system, which can be very useful.  
Our method can also be used for game statistics and basketball training. A basketball video is composed of various actions, and the actions are composed of continuous body posture sequences. LSTM-DGCN can judge changes of action according to the changes of the frame classification results in certain intervals, recognize the current action timely and accurately, and divide the video up automatically. Much labor and time can be saved in dividing the basketball video to count the basketball game score and do action analysis. If  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Action** & **Interval Accuracy (\%)** \\ \hline
01 & Standing dribble & 91.9 \\ \hline
02 & Moving dribble & 78.3 \\ \hline
03 & Front dribble & 83.4 \\ \hline
04 & Behind-the-back dribble & 87.9 \\ \hline
05 & Cross-leg dribble & 77.3 \\ \hline
06 & Turning around & 72.1 \\ \hline
07 & Squat & 80.6 \\ \hline
08 & Overhead pass and catch or shooting & 81.9 \\ \hline
09 & Running with ball & 89.3 \\ \hline
10 & One-hand shot & 88.4 \\ \hline
11 & Chest pass and catch or shooting & 72.7 \\ \hline
12 & Side throwing & 81.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recognition accuracies of all action categories.  
the input is an action sequence that is manually divided, then a faster and more accurate result of action classification can be obtained.  
The basketball action recognition algorithm LSTM-DGCN proposed here achieved good results on our NPU RGB+D dataset. However, it remains unsatisfactory for distinguishing the similar actions of two-handed chest shooting and two-handed chest passing, as well as two-handed overhead passing and two-handed overhead shooting. More training data and more effective features are needed to further improve the accuracy of recognition between similar actions.