基于注意力卷积神经网络的动态手势识别研究  

Study on dynamic gesture recognition based on attention mechanism convolutional neural network

作　　者：施丽红[1] SHI Lihong(Electricity Instilule of Logistics,Jiangsu Vocational College of Business,Nantong 226011,China)

机构地区：[1]江苏商贸职业学院电商与物流学院,江苏南通226011

出　　处：《光学技术》2020年第6期750-756,共7页Optical Technique

摘　　要：针对复杂环境下动态手势识别准确率低的问题,提出了一种基于长短期记忆网络和卷积神经网络的动态手势识别算法。采用长短期记忆网络学习每个滤波器的权重,预测人体外形相关的滤波器组;采用卷积神经网络提取目标手势的轨迹图,创建彩色的轨迹图像;将轨迹图像送入注意力卷积神经网络训练,利用神经网络识别出复杂环境下的手势。实验结果表明,该算法能够准确地检测与跟踪手势的动态变化,并且实现了较好的手势识别准确性。Aiming at the problem of low dynamic gesture recognition accuracy in complex environment, a dynamic gesture recognition technique in complex environment based on attention mechanism convolutional neural network is proposed. First of all, the long short term memory network is adopted to learn the weight of each filter, and predict human appearance correlated filter banks;then, the convolutional neural network is used to extract the trajectory images, and construct a color trajectory image;finally, trajectory images are delivered to attention convolutional neural network to train, the trained neural network is taken advantage to recognize the target gesture in complex environment. Experimental results indicate that the proposed gesture recognition algorithm can detect and track the dynamic gestures, at the same time, it realizes a good gesture recognition accuracy.

关 键 词：长短期记忆网络 手势识别 卷积神经网络 注意力机制 残差神经网络 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]