Multi-Feature Encoder for Radar-Based Gesture Recognition

Yuliang Sun, T. Fei, Xibo Li, Alexander Warnecke, Ernst Warsitz, N. Pohl
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引用次数: 3

Abstract

In this paper, a multi-feature encoder for gesture recognition based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system is proposed to extract the gesture characteristics, i.e., range, Doppler, azimuth and elevation, from the low-level raw data. The radar system updates the hand information for every measurement-cycle on all the scattering centers in its field of view, and our proposed encoder is devised to only focus on those essential scattering centers. After observing the hand over several measurement-cycles, we encode the gesture characteristics sequentially into a 2-D feature matrix, which is successively fed into a shallow convolutional neural network (CNN) for classification. For the purpose of distinguishing relevant gestures, the proposed multi-feature encoder is able to efficiently extract adequate information from a multi-dimensional feature space. Thus, the proposed approach is practical for industrial applications where the available dataset is mostly small-scale. The experimental results show that the proposed multi-feature encoder could guarantee a promising performance for a gesture dataset with 12 gestures.
基于雷达的手势识别多特征编码器
本文提出了一种基于60 GHz调频连续波(FMCW)雷达系统的手势识别多特征编码器,从低电平原始数据中提取手势的距离、多普勒、方位和俯角等特征。雷达系统在每个测量周期都会对其视野内的所有散射中心更新手部信息,而我们所设计的编码器只关注那些重要的散射中心。在观察了几个测量周期后,我们将手势特征依次编码为二维特征矩阵,并将其送入浅卷积神经网络(CNN)进行分类。为了区分相关手势,本文提出的多特征编码器能够有效地从多维特征空间中提取足够的信息。因此,所提出的方法适用于工业应用,其中可用的数据集大多是小规模的。实验结果表明,所提出的多特征编码器可以保证具有12个手势的手势数据集的良好性能。
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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