论文标题
使用卷积神经网络和生成对手网络了解手及
Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks
论文作者
论文摘要
在本文中，引入了一个手势识别系统，以实时识别角色。该系统由三个模块组成：使用卷积神经网络实时跟踪，训练手势和手势识别。用于手动跟踪的凸轮矩算法和手击分析用于获取运动描述符和手部区域。它对背景群集非常健壮，并使用肤色进行手势跟踪和识别。此外，已经提出了这些技术，以提高识别性能和使用训练图像的选择以及自适应阈值手势等方法的准确性，以消除有助于将输入模式作为手势的非手势模式。在实验中，已经对36个手势的词汇进行了测试，包括字母和数字，以及该方法的有效性。
In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.
