CNN Model Design of Gesture Recognition Based on Tensorflow Framework

Zixian Zeng, Qingge Gong, Jun Zhang
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引用次数: 23

Abstract

The paper uses Google newest open-source Tensorflow framework to build the model of gesture recognition, introduces the platform characteristics of Tensorflow, and puts forward a convolution network model based on Tensorflow framework. The experiment is designed with the combination of recognized dataset and self-collected dataset. The experimental results show that the model has high recognition accuracy, high computational efficiency, strong robustness, and can easily adjust the network structure, find the optimal model quickly, and accomplish the task of gesture recognition better.
基于Tensorflow框架的手势识别CNN模型设计
本文采用Google最新的开源Tensorflow框架构建手势识别模型,介绍了Tensorflow的平台特点,提出了基于Tensorflow框架的卷积网络模型。实验采用识别数据集和自采集数据集相结合的方式进行设计。实验结果表明,该模型具有识别精度高、计算效率高、鲁棒性强的特点,能够方便地调整网络结构,快速找到最优模型,较好地完成手势识别任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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