Arm Gesture Recognition using a Convolutional Neural Network

被引：0

作者：

Mathe, Eirini ^{[1
]}

Mitsou, Alexandros ^{[3
]}

Spyrou, Evaggelos ^{[1
,2
,3
]}

Mylonas, Phivos ^{[4
]}

机构：

[1] Natl Ctr Sci Res Demokritos, Inst Informat & Telecommun, Athens, Greece

[2] Technol Educ Inst Sterea Ellada, Dept Comp Engn, Lamia, Greece

[3] Univ Thessaly, Dept Comp Sci, Lamia, Greece

[4] Dept Informat, Corfu, Greece

来源：

2018 13TH INTERNATIONAL WORKSHOP ON SEMANTIC AND SOCIAL MEDIA ADAPTATION AND PERSONALIZATION (SMAP 2018) | 2018年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present an approach towards arm gesture recognition that uses a Convolutional Neural Network (CNN), which is trained on Discrete Fourier Transform (DFT) images that result from raw sensor readings. More specifically, we use the Kinect RGB and depth camera and we capture the 3D positions of a set of skeletal joints. From each joint we create a signal for each 3D coordinate and we concatenate those signals to create an image, the DFT of which is used to describe the gesture. We evaluate our approach using a dataset of hand gestures involving either one or both hands simultaneously and compare the proposed approach to another that uses hand-crafted features.

引用

页码：37 / 42

页数：6

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