Earticle

초록

영어

Recently, hand gesture recognition based on deep 3D convolution neural networks has made great progress. However, the large number of weight parameters that need to be optimized leads to its expensive computational cost. We introduced a transformer-based framework for hand gesture recognition, which is a fully self-attentional architecture. The framework abandons the conventional methods that rely on 3D convolution and proposed an approach to classify actions by focusing on the entire video sequence. In addition, we use a lightweight hand detector to continuously sample the video only when a hand is detected in the video sequence, thus reducing the computational consumption of the system. Experiments on two human hand gesture recognition benchmark datasets show the superiority of the proposed method, compared with existing state-of-the-art methods.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
III. EXPERIMENTS
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Zongjing Cao [ dept. electrical and computer engineering Inha University ]
• Yan Li [ dept. electrical and computer engineering Inha University ]
• Byeong-Seok Shin [ dept. electrical and computer engineering Inha University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004