计算机与现代化Issue(4)：62-67,6.DOI:10.3969/j.issn.1006-2475.2018.04.012
基于可变形卷积神经网络的手势识别方法
Gesture Recognition Method Based on Deformable Convolution Neural Network
摘要
Abstract
Convolution neural network itself has a rich ability of expressing features and learning,but in essence,the module geo-metric transformation ability is fixed.Therefore,the VGG-16 network structure is improved by introducing a deformable convolu-tion kernel,and a convolution neural network structure named DC-VGG is built to study the gesture recognition.In different data sets,the gesture recognition method based on deformable convolution neural network can input RGB image data directly into the network.The results show that the average recognition rate of gestures is over 97%, which can improve the performance of the network,enhance the tolerance and diversity of the convolution neural network to the sample object,and enrich the expression a-bility of the convolution neural network.Compared with the traditional LeNet-5,VGG-16 structure and traditional feature extrac-tion by hand,DC-VGG is deeper than the traditional structure, the robustness is better, the recognition rate is stronger, which can provide reference for the effective recognition of gestures in complex background,and has some extension ability.关键词
手势识别/可变形卷积/卷积神经网络/卷积核/双线性插值Key words
gesture recognition/deformable convolution/convolution neural network(CNN)/convolution kernel/bilinear inter-polation分类
信息技术与安全科学引用本文复制引用
苏军雄,见雪婷,刘玮,华俊达,张胜祥..基于可变形卷积神经网络的手势识别方法[J].计算机与现代化,2018,(4):62-67,6.基金项目
2016年省级大学生创新训练计划项目(201610564356) （201610564356）
广州市科技计划项目(201707010031) （201707010031）
