## 2 Related Work
### Activity Analysis Datasets
#### 2.1.2 Basketball Action Datasets  
The NCAA basketball dataset [16] was collected from 257 NCAA basketball games available on YouTube. The dataset is manually divided into 11 types of activities: three-point success, three-point failure, two-point success, two-point failure, free-throw success, free-throw failure, layup success, layup failure, slam dunk success, slam dunk failure, and steals. Using the NCAA basketball dataset as a benchmark dataset for player detection or event detection is reasonable [17; 18]. The APIDIS basketball dataset [19] was constructed from a basketball game. A unique server captured frames from seven color cameras. All basketball events (shooting, holding, violation, etc.) were manually annotated, and the basketball court measurements, event annotations, and camera calibration data are available in the dataset. This dataset is a complete recording of a basketball game which contains multiple players, so it is suitable as a benchmark for the research on player tracking [20; 21]. The basketball datasets listed above are all from basketball game videos. Basketball game videos often contain multiple basketball players, so confusion of the background and body occlusion are inevitable. In addition, the definitions of action categories in the existing basketball action datasets are often confusing and vague, lacking the theoretical basis of basketball rules and the guidance of professional coaches. As such, these datasets do not qualify as benchmarks for research on basketball player action recognition. As shown in Table 1, our dataset has advantages over the existing datasets in basketball action categories, views, and data modalities. The advantages are summarized as follows: (1) accurate and reasonable classification of multiple basketball actions, (2) more samples for each action class, (3) more intra-class variations (interacted objects, age of actors, etc. ), and (4) depth map and 3D joint point data modalities. In the process of research, we conducted a number of retrievals from UCI Machine Learning Repository, Kaggle Datasets, and Google Datasets. We studied the data modalities, action classifications, and data sizes of the relevant vision-based basketball datasets from the above public datasets, and listed them in Table 1. Other basketball datasets are not listed in Table 1 because they only contain sensor data or game scoring data. For five kinds of datasets in Table 1:  
1. UCF101 and MSR-Action3D are human action recognition datasets. UCF101 does not contain skeleton data so we could not evaluate our method on it. MSR-Action3D contains skeleton data but there are not actions similar to basketball players' actions so we could not evaluate our method on it. 2. NCAA Dataset from Kaggle Datasets and APIDIS Dataset from Google Datasets contain basketball action image data. They contain multiple basketball players and not contain the skeleton data that we need as the input of LSTM-DGCN, so we could not evaluate our method on them. 3. We selected basketball actions and daily actions similar to 12 basic atomic basketball actions from the NTU RGB+D human action recognition dataset to construct the mapping between them, such as walking and moving dribbling, throwing, and passing. Our LSTM-DGCN also achieved the same results with it as the most advanced human action recognition algorithm 2s-AGCN. We propose NPU RGB+D as a benchmark for basketball action recognition research. Compared with other basketball datasets, it has more data modalities and clearer action classification. It can support our research of skeleton-based basketball action recognition