Clothing Detection Action Recognition Based on Hierarchical Transformer Networks
Yixuan Ma, Xiaodong Wang, Jian Yuan, Lianjun Zhang, Jimeng Chen, Keshu Fen, Linyun Yu · 2024
In the application of industrial production operation action analysis based on computer vision, Transformer networks have the problem of large computational overhead and insufficient real-time performance. To address this issue and enable the network to be applied to clothing detection in industrial production operation recognition, a hierarchical Transformer network was constructed using network pooling and unit attention designs, which improved the accuracy of action recognition and focused on computing local information. A cosine re-weighting method was designed to replace the softmax operator in the self-attention mechanism of the Transformer network to reduce network overhead. The robustness of this network was verified on the HMDB51 dataset, achieving an accuracy of 91.7%, with a FLOPs of 42.8 G and 29 M parameters. Compared with other Transformer models, it significantly reduced network overhead while achieving high accuracy. For the analysis of clothing detection actions on a self-built dataset, an edge detection algorithm was added to the network to improve recognition accuracy. The improved network achieved a FLOPs of 22.9 G and 10 M parameters on the self-built clothing detection action dataset, with an accuracy of $\mathbf{9 7. 4 \%}$.