## 4 Methods for Basketball Action Recognition
### Feature-Enhanced LSTM-DGCN
Rather, we use the joint distance calculation method provided in the built-in feature extraction module to calculate the distance from other joint points to the reference point as a substitute so as to unify the skeleton data in different camera coordinate systems. To avoid uneven joint distance caused by the height difference among different basketball players, we further scale the distance between all 3D joint points according to the spine distance between neck and mid-hip. To avoid the lack of direction information and the difficulty of action recognition caused by simply relying on the distance between joints, we also screen out the important joint angles as important features of basketball players' action judgment, such as shoulder joint, elbow joint, and knee joint. Finally, the 0-means method is used to normalize the joint angle and distance. The skeleton data after feature extraction are input into GCN. To further extract the deep action features of basketball players, we need the superposition of GCN layers. For the traditional neural network, the deeper the number of layers, the higher the general accuracy. However, with the deepening of the network, down sampling can lead to the loss of more detailed information, resulting in similar results between the deep network and shallow network. To avoid the gradient disappearing, we introduce the design of residual block with reference to the deep residual network proposed by [PERSON] et al. in [37]. This neural network structure overcomes two problems. That is, the learning rate is low, and the accuracy cannot be effectively improved owing to the deepening of the network depth. The principle of the residual block is shown in Figure 6. The original data output of the previous layers directly skips the layers and is introduced into the input part of the later data layer, which is used as the input of the later network data together with the output of the previous layer after the network processing. \(F(x)\) is a two-layer network without jump connection. The residual block can then be expressed as \(H(x)=F(x)+x\). The residual network is helpful to solve the problem of gradient disappearing and gradient exploding so that we can train a deeper network and guarantee good performance at the same time. The concept of residual is also introduced into the model. If we use G to represent the set of nodes and edges, then the convolution operation of the first graph in the first layer can be expressed as follows:  
\[G_{l+1}=F(G_{l},W_{l}) \tag{6}\]  
When we consider adding residual structure to the GCN model with a depth of more than three layers, we propose a graph residual learning framework, which can learn the underlying mapping \(H\) by fitting another mapping \(F\), and then add the vertices after the transformation to get \(G_{l+1}\). Residual mapping \(F\) learning takes a graph as input and outputs a residual graph for the next level. \[G_{l+1}=H(G_{l},W_{l})=F(G_{l},W_{l})+G_{l} \tag{7}\]  
Over-fitting and over-smoothing are two common problems in deep graph convolution neural networks. Over-fitting weakens the generalization ability of the model to other datasets. Over-smoothing weakens the correlation between output representation and input features with the increase of network depth, which hinders the model from upgrading through training. Therefore, we exert much effort. At the same time, we perform dropout and dropedge as proposed by [PERSON] et al. in [38] for processing on the model. We also discard neural units and association relations randomly during training so as to achieve the effect of data enhancement to offset the influence of over-fitting and over-smoothing. Although our deep graph convolution neural network model can obtain the single-frame action recognition results according to the input skeleton frame sequence, some complex basketball players' actions are difficult to accurately judge by a single frame. It needs to be combined with the before and after time sequence relationship.