基于改进型多维卷积神经网络的微动手势识别方法  

Micro-motion Hand Gesture Recognition Method Based on Improved Multiple Dimensional Convolution Neural Network

作　　者：李玲霞[1] 王羽[1] 吴金君 王沙沙 LI Lingxia;WANG Yu;WU Jinju;WANG Shasha(Chongqing Key Lab of Mobile Communications Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学移动通信技术重庆市重点实验室,重庆400065

出　　处：《计算机工程》2018年第9期243-249,共7页Computer Engineering

基　　金：重庆市基础与前沿研究计划项目(cstc2013jcyj A40032);重庆邮电大学博士启动基金(A2012-33);重庆邮电大学青年科学研究项目(A2013-31)

摘　　要：传统二维卷积神经网络因遗漏时间维度信息导致不能识别微动手势。为此,提出一种基于视频流的微动手势识别方法。对输入视频流进行简单预处理,利用改进型多维卷积神经网络提取手势的时空特征,融合多传感器信息并通过支持向量机实现微动手势识别。实验结果表明,该方法对手势的背景和光照都具有较好的鲁棒性,且针对各类动态手势数据集能达到87%以上的识别准确率。For the traditional Two Dimensional Convolutional Neural Network(2D-CNN),the time dimension information is lost,and thus the dynamic gesture cannot be recognized.This paper proposes a novel dynamic hand gesture recognition method based on video streams,which can effectively improve the overall performance of hand gesture recognition.The input data is simply preprocessed.The spatio temporal feature extraction operation is performed by using improved Multiple Dimensional Convolutional Neural Network(MD-CNN).A multi-sensor fusion method is provided and the dynamic gesture recognition is realized by using Support Vector Machine(SVM).Experimental results show that the proposed method performs well in robustness with respect to the gesture background and illumination.Furthermore,the method achieves the high recognition accuracy beyond 87%for every kind of dynamic gesture dataset.

关 键 词：计算机视觉 手势识别 二维卷积神经网络 多维卷积神经网络 支持向量机 鲁棒性 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]