Home Automation through Hand Gestures Using ResNet50 and 3D-CNN

Ankitha Raksha, Raghul Krishna Rajasekaran, Praveen Francis, Suhas Yogeshwara, Alexander I. Iliev
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引用次数: 1

Abstract

This paper talks about using hand movements for the operations of electrical equipment at home. With the use of the much-advanced algorithms - 3D-CNN and ResNet50 to increase the accuracy in detecting the hand gesture to correctly predict the right motion for the functioning of the electrical device. Eventually, the project focuses on the comparative study between different architectures so that we can determine the best-suited model for these kinds of image detection. We aim to bring about a good accurate model for detecting the hand signals.
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使用ResNet50和3D-CNN通过手势实现家庭自动化
本文讨论了用手部动作来操作家用电器设备。通过使用非常先进的算法-3D-CNN和ResNet50来提高检测手势的准确性,以正确预测电气设备功能的正确运动。最后,该项目侧重于不同架构之间的比较研究,以便我们可以确定最适合这些类型的图像检测的模型。我们的目标是建立一个准确的手部信号检测模型。
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