Research Article
A Dynamic Gesture Recognition Control File Method Based on Deep Learning
@INPROCEEDINGS{10.1007/978-3-030-99191-3_3, author={Fumin Liu and Yuezhong Wu and Falong Xiao and Qiang Liu}, title={A Dynamic Gesture Recognition Control File Method Based on Deep Learning}, proceedings={Cloud Computing. 11th EAI International Conference, CloudComp 2021, Virtual Event, December 9--10, 2021, Proceedings}, proceedings_a={CLOUDCOMP}, year={2022}, month={3}, keywords={Deep learning Gesture recognition Control algorithm}, doi={10.1007/978-3-030-99191-3_3} }- Fumin Liu
Yuezhong Wu
Falong Xiao
Qiang Liu
Year: 2022
A Dynamic Gesture Recognition Control File Method Based on Deep Learning
CLOUDCOMP
Springer
DOI: 10.1007/978-3-030-99191-3_3
Abstract
In order to realize the remote control of the meeting documents in progress, the traditional method uses infrared remote control or 2.4 GHz wireless remote control. However, the shortcomings of carrying and storing the remote control, the infrared itself cannot pass through obstacles or the remote control of the device from a large angle, the 2.4 GHz cost is slightly higher, etc., this article introduces the use of PyTorch model and YOLO network gesture control to facilitate this practical problem. The plan proposes to use the PyTorch model to establish a neural network, train to achieve the purpose of classifying gestures, and use the YOLO network to cooperate with the corresponding control algorithm to achieve the purpose of controlling conference documents. The experimental results show that the proposed scheme is feasible and complete to achieve the required functions.
