Effective Hand Gesture Recognition by Key Frame Selection and 3D Neural Network

• 발행기관: 한국스마트미디어학회
• 저자명: Nguyen Ngoc Hoang Guee-Sang Lee Soo-Hyung Kim Hyung-Jeong Yang
• 간행물 정보: 『스마트미디어저널』Vol9, No.1, 23~29쪽, 전체 7쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2020.03.30

영문 초록

This paper presents an approach for dynamic hand gesture recognition by using algorithm based on 3D Convolutional Neural Network (3D_CNN), which is later extended to 3D Residual Networks (3D_ResNet), and the neural network based key frame selection. Typically, 3D deep neural network is used to classify gestures from the input of image frames, randomly sampled from a video data. In this work, to improve the classification performance, we employ key frames which represent the overall video, as the input of the classification network. The key frames are extracted by SegNet instead of conventional clustering algorithms for video summarization (VSUMM) which require heavy computation. By using a deep neural network, key frame selection can be performed in a real-time system. Experiments are conducted using 3D convolutional kernels such as 3D_CNN, Inflated 3D_CNN (I3D) and 3D_ResNet for gesture classification. Our algorithm achieved up to 97.8% of classification accuracy on the Cambridge gesture dataset. The experimental results show that the proposed approach is efficient and outperforms existing methods.

목차

I. INTRODUCTION

II. PROPOSED METHOD

III. EXPERIMENT AND EVALUATION

IV. CONCLUTION

REFERENCES

키워드

hand gesture recognition dynamic hand gesture key frame extraction action recognition