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.
作者
施丽红
SHI Lihong(Electricity Instilule of Logistics,Jiangsu Vocational College of Business,Nantong 226011,China)
出处
《光学技术》
CAS
CSCD
北大核心
2020年第6期750-756,共7页
Optical Technique
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
long short term memory network
gesture recognition
convolutional neural network
attention mechanism
residual neural networks
