使用深度卷积神经网络模型的自动手势识别

Ishika Dhall, Shubham Vashisth, Garima Aggarwal
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引用次数: 9

摘要

深度学习领域的巨大增长有助于实现计算机视觉应用的突破,特别是在卷积神经网络进入画面之后。cnn独特的结构使其无需任何手动调整即可从输入图像中提取相关信息。今天,有了如此强大的模型,我们有了相当灵活的构建技术,可以改善人类的生活。一种这样的技术可以用于检测和理解各种人类手势,因为它将使人机通信有效。这可能会使传统的输入设备,如触摸屏、鼠标垫和键盘变得多余。此外,与其他设备相比,它被认为是一种高度安全的技术。本文将手势技术与卷积神经网络相结合,构建深度卷积神经网络,构建手势识别应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Hand Gesture Recognition using a Deep Convolutional Neural Network model
The tremendous growth in the domain of deep learning has helped in achieving breakthroughs in computer vision applications especially after convolutional neural networks coming into the picture. The unique architecture of CNNs allows it to extract relevant information from the input images without any hand-tuning. Today, with such powerful models we have quite a flexibility build technology that may ameliorate human life. One such technique can be used for detecting and understanding various human gestures as it would make the human-machine communication effective. This could make the conventional input devices like touchscreens, mouse pad, and keyboards redundant. Also, it is considered as a highly secure tech compared to other devices. In this paper, hand gesture technology along with Convolutional Neural Networks has been discovered followed by the construction of a deep convolutional neural network to build a hand gesture recognition application.
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