Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks

This paper proposes an innovative video behaviour classification method based on pyramid pooling and a variable-scale training strategy, which aims to improve the video behaviour-recognition performance of a 3D convolutional neural network (3D-CNN) and a dual-stream C3D network. By introducing pyramid pooling and secondary pooling operations, the number of pooling layers is optimised, the number of parameters of the model is significantly reduced, and the recognition accuracy is effectively improved. In the improved dual-stream C3D network, the early fusion strategy is adopted to better combine the spatio-temporal features and improve the accuracy of the model. In addition, by introducing the optical flow feature, the model’s perception ability of video dynamic information is enhanced, and the recognition performance is further improved. Experimental results show that the proposed method performs well on multiple video datasets, which is better than the existing mainstream methods, which proves the innovation and efficiency of the proposed method in the field of video behaviour recognition.