@inproceedings{1590b2d23f254be380fbb62b0c1cf7ff,
title = "Gesture recognition algorithm combining ResNet and ShuffleNet",
abstract = "Gesture is a form of non-verbal communication and has many applications, such as sign language communication between deaf and dumb people, robot control, human-computer interaction and medical applications. The commonly used acquisition equipment in gesture recognition is the visible light camera, but illumination has a great impact on the accuracy of the collected data classification processing. The whole project designed a complete end-to-end edge computing system design and deployment, the system can achieve from gesture image acquisition to gesture recognition. A dataset of 3600 thermal images was created, and each gesture had 1200 thermal images with only 4∗4 resolution. These images were upsampled by bilinear interpolation and fed into a new lightweight deep learning model combining deep residual learning with ShuffleNet V2 for gesture classification. The system achieved 98.63\% accuracy on the test data set. Another advantage is that it is based on thermal imaging, so the accuracy is not affected by background lighting conditions.",
keywords = "Deep learning, Gesture recognition, The neural network, Thermal imaging",
author = "Zhengjiang Xie and Li Lou and Kunpeng Jia and Binbin Jiao",
note = "Publisher Copyright: {\textcopyright} SPIE.; 2021 International Conference on Internet of Things and Machine Learning, IoTML 2021 ; Conference date: 17-12-2021 Through 19-12-2021",
year = "2022",
doi = "10.1117/12.2628655",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Pushpendu Kar and Steven Guan",
booktitle = "International Conference on Internet of Things and Machine Learning, IoTML 2021",
}