Hand Gesture Recognition for Smart Television Using GRU

被引：0

作者：

Suresh, Garugu Yaswanth Lakshmi ^{[1
]}

Sravani, Ravinuthala Gayatri Venkata ^{[1
]}

机构：

[1] VIT AP Univ, Sch Comp Sci & Engn, Inavolu AP Secretariat, Amaravati, Andhra Pradesh, India

来源：

PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON DATA SCIENCE, MACHINE LEARNING AND APPLICATIONS, VOL 1, ICDSMLA 2023 | 2025年 / 1273卷

关键词：

Smart Television; Image Processing; Deep Learning; Convolutional Neural Networks; Gated Recurrent Unit (GRU);

D O I：

10.1007/978-981-97-8031-0_67

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Even with the rapid advancement of technology, using a television still requires a physical remote control. Apart from occasionally losing sight of the television remote control, we also sometimes run out of batteries. The goal is to discover an effective way of controlling television and develop 3D hand gesture-based smart television control using Gated Recurrent Unit (GRU). The results of the research show that hand gesture recognition-based interface technology is capable of performing the majority of smart TV operations. It is a comfortable and delightful experience for consumers. The existing research features static image gesture recognition with predefined models for training in addition to the existing research the proposed model features sample video dataset, Custom design with Gated Recurrent Unit. The suggested model is trained using five hand gestures. The camera positioned on the TV continually records the motions. Each gesture is associated with a certain command. The proposed model has achieved an accuracy of about 94% in recognizing the gestures.

引用

页码：624 / 634

页数：11

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