Dynamic hand gesture classification based on attention mechanism and transformer

In the era of rapid development of deep learning, the popular transformer in recent years has played an outstanding role in many fields, but most of it focuses on the development of language models, and is still in the development stage in computer vision. Gesture recognition is a relatively popular module in computer vision, but the mainstream method still uses CNN for gesture recognition, and there are few articles combining gesture recognition with attention mechanism and transformer. In this paper, we propose a dynamic gesture recognition model based on attention mechanism and Transformer. In order to extract the valid information in each frame, we add the attention mechanism to the feature extraction network, followed by passing it into the transformer to predict the hand gesture through the self-attention mechanism. The model has been tested on the IsoGD dataset, achiving good experimental results. Not only do we confirm that the attention mechanism can improve the recognition accuracy through ablation study, but also prove that transformer is feasible to process and identify the temporal information among frames in dynamic gestures and perform recognition.