Gesture Recognition Method Based on Sim-ConvNeXt Model

Gesture recognition has become an important subject for researchers dealing with the problem of human-computer interaction, with computer vision enabling significant advancements in this field. Existing gesture recognition methods based on vision typically rely on traditional pure convolutional neural networks. However, these models face challenges in terms of low feature extraction efficiency and poor representation ability when encountering complex backgrounds, leading to complex parameter optimization. To address this issue, we propose an improved ConvNeXt algorithm, which is called as Sim-ConvNeXt network. SimAM (A Simple, Parameter-Free Attention Module for Convolutional Neural Networks) was introduced to address the issue of complex gesture recognition environments and unbalanced feature coupling between gestures. Additionally, the optimal fusion mode of SimAM was analyzed to resolve the problem of feature information loss between channels after the Depthwise Convolution module in the ConvNeXt model. Finally, the experiment on the HaGRID dataset showed that our model improved the identification accuracy effectively with a very small increase in the number of parameters, and achieved better performance compared with other models.