Basketball player action recognition based on improved LSTM neural network

In order to improve the IoU value and accuracy of basketball player action recognition methods, this paper proposes a basketball player action recognition method based on an improved LSTM neural network. Firstly, establish a coordinate system in the visual system and perform appropriate sequence transformations on the collected basketball player action images to complete image acquisition. Next, a Kalman filter is used to filter and process the collected action images. Finally, based on the LSTM neural network unit, two sigmoid gating units are introduced to improve it. Using the filtered action image as input and the action recognition result as output, an improved LSTM neural network is used to construct an action recognition model and obtain the recognition result. The experimental results show that the proposed method has achieved significant improvement in IoU value and accuracy in action recognition, with the highest recognition accuracy reaching 98.26%.