Abstract: Aiming at the problem of low recognition accuracy and high misclassification rate of traditional action recognition methods in dealing with complex bird action patterns, an enhanced deep learning model is proposed. The model integrates a multiple-excitation module and pyramid split attention to improve 3D residual networks, aiming to improve both the accuracy and efficiency of bird action recognition. The inter-frame difference method is utilized to effectively reduce the computational burden while preserving critical spatio-temporal information, thereby improving the recognition accuracy. The introduction of a multiple-excitation module improves the original residual block so that the model can accurately capture subtle motion action features, which solves ambiguities in recognizing complex dynamic actions of birds. Additionally, the original 3D convolutional layer is replaced with 3D pyramid split attention to achieve effective capture of bird action features at different scales. Experiments conducted on a self-built bird action video dataset demonstrate a high recognition accuracy of 90.48%, which significantly outperforms the baseline model and other existing popular action recognition networks. These results confirm that the model can effectively handle the complex bird action recognition task.

Key words: bird action recognition, multiple excitation, pyramid split attention, interframe difference method, self-built dataset