한국인터넷정보학회 논문지
Current Result Document :
| 한글제목(Korean Title) |
CNN-based Gesture Recognition using Motion History Image |
| 영문제목(English Title) |
CNN-based Gesture Recognition using Motion History Image |
| 저자(Author) |
Youjin Koh
Taewon Kim
Min Hong
Yoo-Joo Choi
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| 원문수록처(Citation) |
VOL 21 NO. 05 PP. 0067 ~ 0073 (2020. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this paper, we present a CNN-based gesture recognition approach which reduces the memory burden of input data. Most of the neural network-based gesture recognition methods have used a sequence of frame images as input data, which cause a memory burden problem. We use a motion history image in order to define a meaningful gesture. The motion history image is a grayscale image into which the temporal motion information is collapsed by synthesizing silhouette images of a user during the period of one meaningful gesture. In this paper, we first summarize the previous traditional approaches and neural network-based approaches for gesture recognition. Then we explain the data preprocessing procedure for making the motion history image and the neural network architecture with three convolution layers for recognizing the meaningful gestures. In the experiments, we trained five types of gestures, namely those for charging power, shooting left,shooting right, kicking left, and kicking right. The accuracy of gesture recognition was measured by adjusting the number of filters in each layer in the proposed network. We use a grayscale image with 240 x 320 resolution which defines one meaningful gesture and achieved a gesture recognition accuracy of 98.24%.
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| 키워드(Keyword) |
Gesture recognition
neural network
convolutional neural network
motion history image
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| 파일첨부 |
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