计算机工程2017，Vol.43Issue(8)：243-248,6.DOI:10.3969/j.issn.1000-3428.2017.08.041
基于多列深度3D卷积神经网络的手势识别
Hand Gesture Recognition Based on Multi-column Deep 3D Convolutional Neural Network
摘要
Abstract
The feature extraction method adopted by traditional Convolutional Neural Network(CNN)for video image with continuous frames is east to lose movement information on the target time axis,resulting in low recognition accuracy.To solve this problem,a method based on multi-lolu deep 3D is proposed.The 3D convolution kernel is used to extract the temporal and spatial features to capture the object's motion information.In order to avoid the error classification because of the insufficient feature information of single 3D CNN,the multi-column 3D CNN is consisted by multi-component 3D CNN that each of them has very strong classification ability.The output of this structure is weighed by the output of each of the 3D CNN,and the category which has the maximum weight is determined to be the final result.The structure of multi-column 3D CNNs is applied to the CHGD for hand gesture recognition.Experimental results show that the method achieves a recognition rate of 95.09％,and the recognition rate compared to a single 3D CNN increases by nearly 7％,it increases by nearly 20％compared to the traditional 2D CNN,it has very excellent recognition ability for the video image sequence.关键词
视频图像序列处理/手势识别/深度学习/特征提取/卷积神经网络/运动目标识别Key words
video image sequence processing/hand gesture recognition/deep learning/feature extraction/Convolutional Neural Network(CNN)/moving object recognition分类
信息技术与安全科学引用本文复制引用
易生,梁华刚,茹锋..基于多列深度3D卷积神经网络的手势识别[J].计算机工程,2017,43(8):243-248,6.基金项目
国家自然科学基金青年基金(61203374) （61203374）
陕西省自然科学基金国际合作项目(2014KW01-05). （2014KW01-05）
