Hand Motion Analysis using CNN

Harsh Raj, Aditya Duggal., M. Shetty, Sreekanth Uppara, M. Srividya
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引用次数: 1

Abstract

-Hand motion detection and gesture recognition research has attracted large interest due to its wide range of applications in the field of Human computer interaction such as sign language recognition, 3D printing, virtual reality. There have been several approaches to create a robust algorithm to ease human computer interaction and perform in unfavourable environments. The real time recognition and learning of the model are big challenges. In this work, we use Convolutional Neural Network architecture to detect and classify hand motions, the region of interest of the image is passed through the neural network for the hand motion analysis and detection. Our system has achieved testing accuracy of 98%.
使用CNN进行手部动作分析
手部运动检测和手势识别由于其在人机交互领域的广泛应用,如手语识别、3D打印、虚拟现实等,引起了人们的广泛关注。有几种方法可以创建一个健壮的算法来简化人机交互并在不利的环境中执行。模型的实时识别和学习是一个巨大的挑战。在这项工作中,我们使用卷积神经网络架构来检测和分类手部运动,将图像的感兴趣区域通过神经网络进行手部运动分析和检测。该系统的检测准确率达到98%。
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