Leap Motion Hand Gesture Recognition Based on Deep Neural Network
Autor: | Qinglian Yang, Dongdong Zhao, Shi Yan, Xingwen Zhou, Ding Weikang |
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Rok vydání: | 2020 |
Předmět: |
Scheme (programming language)
0209 industrial biotechnology Artificial neural network Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 020901 industrial engineering & automation Gesture recognition Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business computer Gesture computer.programming_language |
Zdroj: | 2020 Chinese Control And Decision Conference (CCDC). |
DOI: | 10.1109/ccdc49329.2020.9164723 |
Popis: | This paper proposes a new gesture recognition system based on Deep Neural Network (DNN) and Leap Motion. The palm model is reconstructed to obtain the feature data, and then the feature data of all experimenters are obtained by the Leap Motion controller. The data are finally put into the DNN model for training to implement the recognition of specific hand gesture after being normalized. By testing with 30000 gesture frames of five volunteers, it is found that the recognition accuracy of the proposed scheme can reach 98%, which indicates that the scheme can complete the specific gesture recognition with a high average recognition rate. |
Databáze: | OpenAIRE |
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