计量学报2024，Vol.45Issue(6)：795-805,11.DOI:10.3969/j.issn.1000-1158.2024.06.04
基于DSConvBiGRU网络和热电堆阵列的动态手势识别方法
Dynamic Gesture Recognition Method Based on DSConvBiGRU Network and Thermopile Array
摘要
Abstract
The DSConvBiGRU network model which is suitable for embedded systems and combines depthwise separable convolutional neural networks and bidirectional gated recurrent units for the classification of dynamic gesture sequences is proposed.A dynamic gesture recognition solution which utilizes a low-resolution thermopile array sensor is designed and implemented.An experimental dataset comprising various dynamic gestures has been constructed and publicated on open website.The deployment of the pre-trained network model on the Raspberry Pi edge device has been accomplished.The system preprocesses 20 consecutive temperature matrices exported by the sensor through interval mapping,background subtraction,Lanczos interpolation,and Otsu thresholding to obtain a single dynamic gesture sequence.Subsequently,the pre-trained DSConvBiGRU network is employed for the classification.Experimental results demonstrate that the network model achieves an accuracy of 99.291％on test dataset.The time comsunption of preprocess and inference on the edge device is 5.513 ms and 8.231 ms respectively.The system meets the design requirements for low-power consumption,high precision,and real-time performance.关键词
机器视觉/光电检测/动态手势识别/热电堆阵列/深度可分离卷积神经网络/双向门控循环单元Key words
machine vision/photoelectic detection/dynamic gesture recognition/thermopile array/depthwise separable convolutional neural networks/bidirectional gated recurrent unit引用本文复制引用
顾亮,于莲芝..基于DSConvBiGRU网络和热电堆阵列的动态手势识别方法[J].计量学报,2024,45(6):795-805,11.基金项目
国家自然科学基金(61603257) （61603257）
