Dynamic hand gesture classification based on attention mechanism and transformer

Zhen Ren; Haonan He; Zhengyi Huang

doi:10.1117/12.2684193

1 August 2023 Dynamic hand gesture classification based on attention mechanism and transformer

Zhen Ren, Haonan He, Zhengyi Huang

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127542R (2023) https://doi.org/10.1117/12.2684193
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

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.

Citation Download Citation

Zhen Ren, Haonan He, and Zhengyi Huang "Dynamic hand gesture classification based on attention mechanism and transformer", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127542R (1 August 2023); https://doi.org/10.1117/12.2684193

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available