Application research of gesture recognition based on lightweight neural network

Xinghan Huang; Xiaofu Du; Hedan Liu

doi:10.1117/12.2653442

8 December 2022 Application research of gesture recognition based on lightweight neural network

Xinghan Huang, Xiaofu Du, Hedan Liu

Proceedings Volume 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022); 124741I (2022) https://doi.org/10.1117/12.2653442
Event: Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 2022, Guilin, China

Abstract

With the promotion of smart city and other technologies, the application of embedded vision detection equipment is becoming more and more popular, among which gesture recognition is an important application of embedded vision detection equipment. At present, gesture recognition technology on embedded visual detection equipment is mostly implemented by calling API in domestic and foreign researches and products. But this method needs the support of stable communication network and has certain delay problem. To solve the above problems, this paper proposes a lightweight neural network model that can be deployed on embedded devices, which can realize local gesture recognition on embedded terminals without network remote transmission. The network builds a training framework on PyTorch and uses a homemade dataset for training, then lightens the network and finally deploys on raspberry PI for gesture recognition. Experimental results show that this network can run at a higher rate in raspberry PI 4B (4GB), and the model size is greatly reduced. The final recognition effect is good, and it has high practical value.

Citation Download Citation

Xinghan Huang, Xiaofu Du, and Hedan Liu "Application research of gesture recognition based on lightweight neural network", Proc. SPIE 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 124741I (8 December 2022); https://doi.org/10.1117/12.2653442

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