Gesture recognition algorithm combining ResNet and ShuffleNet

Zhengjiang Xie; Li Lou; Kunpeng Jia; Binbin Jiao

doi:10.1117/12.2628655

22 April 2022 Gesture recognition algorithm combining ResNet and ShuffleNet

Zhengjiang Xie, Li Lou, Kunpeng Jia, Binbin Jiao

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121741B (2022) https://doi.org/10.1117/12.2628655
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

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.

Citation Download Citation

Zhengjiang Xie, Li Lou, Kunpeng Jia, and Binbin Jiao "Gesture recognition algorithm combining ResNet and ShuffleNet", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121741B (22 April 2022); https://doi.org/10.1117/12.2628655

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KEYWORDS

RGB color model

Thermography

Data modeling

Gesture recognition

Performance modeling

Thermal modeling

Convolution

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