## 1 Introduction  
With the development of computer vision and deep learning technology, the applications of depth cameras in human-computer interactions, automatic driving, virtual reality, and so on have increased in number. One of the most popular application scenarios is the use of 3D vision for human action recognition and behavior analysis [1]. Human action recognition datasets in RGB image data and Depth data (RGB+D) format have also emerged [2]. However, only a few large-scale open RGB+D datasets are dedicated to basketball to support research on basketball player action recognition. As a result, we cannot build a benchmark that can evaluate and compare basketball player action recognition methods. Recent data-driven technology, such as deep learning methods, has a high demand for data. Nowadays, almost all related datasets have limitations in the following aspects. First, a huge gap exists between general human action recognition datasets and professional basketball action recognition datasets. Although many large human action recognition datasets contain basketball action data such as dribbling and shooting, as is shown in [3], they are far from adequate to support data-driven methods. To an extent, some daily actions are similar to those of basketball players, such as walking and dribbling and carrying and holding, but the relationships are rare and far-fetched. In addition, the introduction of nonstandard actions as sample data can negatively influence the result of action recognition. Second, no absolute action standard exists in basketball, and all basketball actions that conform to the rules are allowed. For example, different basketball players can choose their own habitual shooting action according to their own situations to help improve the hit rate, so the shooting actions of different basketball players may vary greatly. In other words, the actions of basketball players often have the characteristics of their own styles [4]. Therefore, the classification of basketball players' actions is very challenging, so as much video data as possible from different players is necessary to improve the reliability and versatility of the dataset. In addition, different actions may have the same pattern, such as dunking, layups, passing, and catching, so complete sequences and accurate marking are important. Third, the deficiency of basketball player action datasets restricts the application of the most advanced data-driven learning methods to the problem of basketball action recognition. The superiority of the deep learning algorithms in the field of action recognition has been fully described in [5]. So far, despite the many attempts, the problem of over fitting remains, so we have to narrow the range of learning parameters. We need more samples to train a classification network or model. To overcome these difficulties, we defined 32 kinds of atomic actions that can cover most of the complex basketball actions and constructed a new large-scale dataset for a 3D basketball player action analysis. This dataset is composed of RGB+D videos of 12 basketball actions performed by 10 subjects captured by the ZED Stereo Camera launched by Stereolabs. We processed the original 3D video data and obtained depth maps, left and right view frame sequences, and 3D coordinates of 25 key points of the human body. In our dataset, basketball players of different ages and heights performed dribbling (standing dribble, moving dribble, front dribble, cross-leg dribble, etc. ), passing and catching (chest passing, overhead passing, chest catching, overhead catching, etc. ), and shooting (one-hand shooting, chest shooting, overhead shooting, etc.). The 12 kinds of actions in our dataset cover most of the common technical actions in basketball games, which makes the dataset abundant and diverse. Owing to the uncontrollability of outdoor court background and lighting, our dataset was collected in the standard indoor basketball court.