DYNAMIC GESTURE DESIGN AND RECOGNITION FOR HUMAN-ROBOT COLLABORATION WITH CONVOLUTIONAL NEURAL NETWORKS

被引：0

作者：

Chen, Haodong ^{[1
]}

Tao, Wenjin ^{[1
]}

Leu, Ming C. ^{[1
]}

Yin, Zhaozheng ^{[2
,3
]}

机构：

[1] Missouri Univ Sci & Technol, Dept Mech & Aerosp Engn, Rolla, MO 65409 USA

[2] SUNY Stony Brook, Dept Biomed Informat, Stony Brook, NY 11794 USA

[3] SUNY Stony Brook, Dept Comp Sci, Stony Brook, NY 11794 USA

来源：

PROCEEDINGS OF THE 2020 INTERNATIONAL SYMPOSIUM ON FLEXIBLE AUTOMATION (ISFA2020) | 2020年

基金：

美国国家科学基金会;

关键词：

Human-robot collaboration; Dynamic gesture recognition; Motion History Image; Convolutional Neural Networks; MOTION; AUGMENTATION; SYSTEM; AWARE;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：8

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