基于全局频域池化的行为识别算法.

The current 3D-ConvNet-based action recognition algorithms generally use GAP to compress feature information. However, it leads to issues of information loss, redundancy, and network overfitting. To address these issues and enhance the retention of high-level semantic information extracted by the convolutional layer, this paper proposed an action recognition al- gorithm based on GFDP. Firstly, DCT shows that GAP is a special case of feature decomposition in the frequency domain. Therefore, the algorithm introduced more frequency components to increase the specificity between feature channels and reduce the information redundancy after information compression. Secondly, to better suppress the overfitting problem, the algorithm introduced the batch normalization strategy to the convolutional layer and extended it to the fully connected layer of the action recognition model with ERB-Res3D as the skeleton to optimize the data distribution. Finally, this paper verified the proposed method on the UCF101 dataset. The results reveals that the model's computational load is 3.5 GFlops, with 7.4 million para- meters. The final recognition accuracy improved by 3.9% based on the ERB-Res3D model and 17.4% based on the original Res3D model. This improvement effectively achieves more accurate behavior recognition results. [ABSTRACT FROM AUTHOR]