Design of Unmarked AI Recognition Algorithm for Athletes’ Traditional Sports Actions Based on Attention Mechanism

In order to accurately and quickly understand the characteristics and skills of athletes’ traditional sports actions, an unmarked AI recognition algorithm for athletes’ traditional sports actions based on attention mechanism is designed. The lightweight space-time map convolution neural network (ST-GCN) based on attention mechanism in AI technology is used to complete the traditional sports action recognition of athletes. The traditional sports action skeleton map of athletes is constructed as unlabeled samples and input into the ST-GCN network. The time and space characteristics of the input skeleton map are extracted through the time convolution network (TCN) and graph convolution neural network (GCN), respectively. Add graph attention mechanism and channel attention mechanism in the network layer and channel, improve the feature expression ability and action recognition accuracy, and introduce Ghost module to replace the original image convolution work, complete the network lightweight processing, and improve the efficiency of ST-GCN network recognition of sports actions. Complete the classification and recognition of athletes’ traditional sports actions through the standard SoftMax. Experiments show that when the size of the attention mechanism window added in ST-GCN is 1000, the network can have the best performance, and the identified resource cost is relatively minimal. After adding attention mechanism, the F1 score of ST-GCN network in unmarked training is close to 1. The skeleton extraction results of athletes are very accurate, and the algorithm can accurately identify different actions generated by different kinds of movements.