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计算机系统应用:2020,29(9):225-230
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改进卷积神经网络的动态手势识别
(江南大学 轻工过程先进控制教育部重点实验室, 无锡 214122)
Improved Dynamic Gesture Recognition Method Based on Convolutional Neural Network
(Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi 214122, China)
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投稿时间:2019-12-30    修订日期:2020-01-22
中文摘要: 针对现有的单目视觉下动态手势识别率低、识别手势种类少等问题提出一种联合卷积神经网络和支持向量机分类(CNN-Softmax-SVM)的动态手势识别算法.首先采用一种基于YCbCr颜色空间和HSV颜色空间的快速指尖检测跟踪,能在复杂背景下实时获取指尖运动轨迹;其次将指尖运动轨迹作为联合CNN-Softmax-SVM网络的输入,最终通过训练网络来识别动态手势.测试结果显示,采用联合CNN-Softmax-SVM算法能够很好地识别动态手势.
Abstract:A dynamic gesture recognition algorithm based on convolutional neural network and support vector machine classification (CNN-Softmax-SVM) is proposed to solve the problems of low recognition rate and few gesture recognition types in monocular vision. Firstly, the fast fingertip detection and tracking algorithm based on YCbCr and HSV color space is employed, which can acquire fingertip trajectory in real time under complex background. Secondly, fingertip trajectory is used as input of joint CNN-Softmax-SVM network, and finally dynamic gesture trajectory is recognized by trained network. The test results show that the combined CNN-Softmax-SVM algorithm can identify the dynamic gesture trajectory well.
文章编号:7546     中图分类号:    文献标志码:
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引用文本:
付天豪,于力革.改进卷积神经网络的动态手势识别.计算机系统应用,2020,29(9):225-230
FU Tian-Hao,YU Li-Ge.Improved Dynamic Gesture Recognition Method Based on Convolutional Neural Network.COMPUTER SYSTEMS APPLICATIONS,2020,29(9):225-230

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