The present invention discloses a method for identifying terrestrial
mammal actions based on a spatiotemporal channel attention mechanism, and belongs to the technical field of
deep learning action recognition. The present invention first sets an
action recognition model, which includes a 3D
backbone network for extracting spatiotemporal features of a video, a 2D
backbone network for extracting spatial features of a
key frame of a video, and a channel fusion module for fusing spatiotemporal features and spatial features, and its output is input into an attention mechanism module after a first
convolution block, and the attention feature map output by the attention mechanism module is sent to a classification layer through a second
convolution block, and the classification layer is used to perform
action recognition prediction on the classification feature map, and obtain action recognition prediction results, including: terrestrial
mammal action category, confidence of a prediction box, and location information of a prediction box; based on the collected training
data set, the action recognition model is deep-learned and trained, and when a preset convergence condition is met, an action recognition model for a target animal is obtained; the total
loss function used when the action recognition model is deep-learned and trained is a weighted sum of a prediction box regression function, a classification function, and a confidence function. Based on the existing spatiotemporal action recognition model based on
deep learning, the present invention optimizes the
backbone network architecture,
loss function composition, etc., which to a certain extent overcomes the problems of
small sample actions and difficulty in recognizing easily confused actions in the original model, enhances the model's generalization ability and the recognition accuracy of terrestrial mammals, and improves
detection performance.