Research on basketball emergency stop jump shot action recognition based on a semantic guided neural network

In order to accurately and quickly recognise basketball emergency stop and jump shot movements, a new semantic guided neural network-based basketball emergency stop jump shot action recognition method is proposed. Firstly, improve the quality of basketball action images through colour vectorisation and filtering pre-processing techniques. Secondly, using image retrieval technology for edge contour feature extraction and fusion retrieval, a high suspicion basketball emergency stop jump shot action pixel feature sample set is selected. Finally, semantic information is integrated into the neural network to improve recognition accuracy. The network architecture innovatively incorporates non local feature extraction modules, ECA attention mechanism modules, and deformable convolution modules to extract feature information. Through fully connected layers, accurate recognition of basketball emergency stop jump shots is achieved. The test results show that the recognition accuracy of this paper method is stable at around 95%, and the highest recognition time is only 0.93 s.