## 7 Conclusions  
In this paper, research on the action recognition of basketball players was presented. We provided an action classification method for basketball players and constructed a rule-based large-scale basketball dataset NPU RGB+D which contains 2169 RGB+D videos and 75,000 frames, including an RGB frame sequence, a depth map, and skeleton coordinates, covering the most basic 12 types of basketball actions. We also proposed a feature-enhanced method LSTM-DGCN of action recognition based on the spatio-temporal relationship of skeleton data. Moreover, we provided three evaluation criteria under different scenarios. Using our NPU RGB+D dataset, we compared RGB video-based baseline methods I3D and TSN, the depth map-based baseline method HON4D, and skeleton-based methods ST-GCN and 2s-AGCN. With our method, we achieved 80.90%, 81.60%, and 83.70% accuracies in cross validation, cross-subject evaluation, and cross-view evaluation, which are higher values than those achieved by the other human action recognition methods. On the open NTU RGB+D dataset, our LSTM-DGCN method achieved 87.0% and 94.6% accuracies under cross-subject and cross-view modes, respectively--close to the 88.5% and 95.1% accuracy of the state-of-the-art human action recognition method 2s-AGCN. The experimental results show that the proposed basketball player action dataset contains appropriate action categories and rich scenes and objects, which is challenging for some classic action recognition methods. Our dataset can also be used as a benchmark dataset for basketball action recognition. Our proposed action recognition method makes full use of the temporal and spatial characteristics of player skeleton sequence and had good performance under all evaluation criteria of different benchmark datasets. Our method exhibits good robustness and adaptability. We hope that our proposed basketball action recognition dataset can become a challenging benchmark dataset in the field of action recognition. We also hope that our research on basketball action recognition method can provide inspiration to our peers in the field of action recognition. At present, the 12 types of actions performed by 10 players with 2169 RGB+D 21 videos and 75,000 frames in our open NPU RGB+D dataset are the results of four attempts at shooting. The dataset includes RGB frame sequences, depth maps, and skeleton coordinates. At the same time, we put forward a feature-enhanced LSTM-DGCN method for action recognition of basketball players. Due to the influences of the site, professionals, and environment, we only selected high-quality RGB+D videos from a large number of datasets for preprocessing. What is more, this involved the acquisition process of L&R view frame sequences, depth maps, and skeleton joint point coordinates; video segmentation; interval division; category marking; and coordinate extraction, which took our team 6 months. Hence, there are difficulties in data collection and processing. We chose 12 basic atomic actions for experiments to give a benchmark dataset and verify the effectiveness of our method. Our experimental data collection and processing process is worth learning, and it is sufficient to verify our method. By repeating our current technical path, future work can identify more basketball players' actions (i.e., other kinds of atomic actions in Section 3.1), to apply our method more effectively for game statistics and basketball training in practice. In future work, we will further expand the dataset of basketball player's actions to verify the effectiveness of our method and publish it to the public. Colleagues in related research fields are welcome to supplement our work and improve on it. In the future, we will conduct real-time tracking and action recognition research on multiple basketball players on the court. [PERSON] et al. proposed a unified RGBD tracking evaluation benchmark Princeton tracking benchmark in [43] to quantitatively compare tracking algorithms based on RGB and RGBD.