Arm Gesture Recognition using a Convolutional Neural Network

Eirini Mathe, Alexandros Mitsou, E. Spyrou, Phivos Mylonas
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引用次数: 9

Abstract

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.
使用卷积神经网络的手臂手势识别
在本文中,我们提出了一种使用卷积神经网络(CNN)的手臂手势识别方法,该方法是在原始传感器读数产生的离散傅里叶变换(DFT)图像上进行训练的。更具体地说,我们使用Kinect RGB和深度相机来捕捉一组骨骼关节的3D位置。我们从每个关节为每个3D坐标创建一个信号,并将这些信号连接起来创建一个图像,该图像的DFT用于描述手势。我们使用同时涉及一只手或两只手的手势数据集来评估我们的方法,并将所提出的方法与另一种使用手工制作特征的方法进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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