Dynamic Gesture Design and Recognition for Human-Robot Collaboration With Convolutional Neural Networks
Dynamic Gesture Design and Recognition for Human-Robot Collaboration With Convolutional Neural Networks
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DOI：
10.1115/isfa2020-9609
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Haodong Chen;Wenjin Tao;M. Leu;Zhaozheng Yin
中科院分区：
文献类型：
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作者：
Haodong Chen;Wenjin Tao;M. Leu;Zhaozheng Yin
人机协作是现代工业中的一项具有挑战性的任务，人机协作中的手势通信引起了人们的广泛兴趣。提出并实现了一种基于运动历史图像（MHI）和卷积神经网络（CNN）的动态手势识别系统。首先，设计了10个动态手势，用于人类工人与工业机器人进行通信。其次，采用MHI方法从视频片段中提取手势特征，并生成动态手势的静态图像作为CNN的输入。最后，构建了用于手势识别的CNN模型。实验结果表明，非常有前途的分类精度使用这种方法。
Human-robot collaboration (HRC) is a challenging task in modern industry and gesture communication in HRC has attracted much interest. This paper proposes and demonstrates a dynamic gesture recognition system based on Motion History Image (MHI) and Convolutional Neural Networks (CNN). Firstly, ten dynamic gestures are designed for a human worker to communicate with an industrial robot. Secondly, the MHI method is adopted to extract the gesture features from video clips and generate static images of dynamic gestures as inputs to CNN. Finally, a CNN model is constructed for gesture recognition. The experimental results show very promising classification accuracy using this method.