基于卷积神经网络的误判手势识别方法

Kaiyun Sun, Zhiquan Feng, Changsheng Ai, Yingjun Li, Jun Wei, Xiaohui Yang, Xiaopei Guo
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引用次数: 0

摘要

基于Kinect 2.0,建立了17种静态手势库,并采用卷积神经网络进行训练。对每个手势的分类已经做了大量的统计实验。在实验过程中,我们发现了一个现象,17个手势中有几个手势很容易混淆。为了便于描述,我们称这些手势为相似手势。从大数据的角度出发,假设卷积神经网络模型的测试结果满足大数定理。因此,针对误判手势,本文提出了一种基于概率统计的识别方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Recognition Method of Misjudgment Gesture Based on Convolutional Neural Network
Based on the Kinect 2.0, 17 kinds of static gesture libraries were established and trained by Convolutional Neural Network. A lot of statistical experiments have been done on the classification of each gesture. During the experiment, we found a phenomenon that several gestures in the 17 gestures were easily confused. And for the sake of description, we call these gestures as similarity gestures. It is assumed that the test result of convolutional neural network model satisfies the large number theorem from the angle of large data. Therefore, For misjudgment gestures, this paper presents a recognition method based on probability statistics.
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