Dynamic gesture recognition method based on convolutional neural network

被引：0
|
作者：
Xu, Xiaoyu [1 ]
Deng, Lizhen [1 ]
Meng, Qingmin [1 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Peoples R China
来源：
2019 INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA) | 2019年
关键词：
human-computer interaction; gesture recognition; skin segmentation; inter-frame difference; convolutional ceural networks;
D O I：
10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00085
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A method for identifying dynamic gestures is studied for the problem of low recognition accuracy under the rotation angle in gesture recognition of human-computer interaction. The methodology of the design is summarized in four processes. Firstly, the inter-frame difference method is used to extract the gesture key frame. Secondly, Hue Saturation Value (HSV) color space model is used to achieve gestures positioning. Thirdly, gesture key frame and final gesture frame are stitched together. In the final process, training and testing data using convolutional neural network model. The experimental results show that the recognition accuracy of the proposed five kinds of gesture models can reach 92% The method can better realize the recognition of the gesture under the rotation angle.
引用
收藏
页码：389 / 394
页数：6
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