基于卷积神经网络的手势识别

S. Shanmugam, Lakshmanan S A, P. Dhanasekaran, P. Mahalakshmi, A. Sharmila
{"title":"基于卷积神经网络的手势识别","authors":"S. Shanmugam, Lakshmanan S A, P. Dhanasekaran, P. Mahalakshmi, A. Sharmila","doi":"10.1109/i-PACT52855.2021.9696463","DOIUrl":null,"url":null,"abstract":"The implicit message is usually brought to the spectators through activities involving various body parts like hands, face and arms. This is prominently known as Gesture and many such gestures are generally performed through hands in an involuntary manner. Smartness is to keep tabs on these hand gestures and derive purposeful details out of it. Convolutional neural networks (CNN) track these complex movements and help in extracting prime features. In this paper, training and testing were done consecutively with the aid of images to check the effectiveness of CNN and results are presented.","PeriodicalId":335956,"journal":{"name":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hand Gesture Recognition using Convolutional Neural Network\",\"authors\":\"S. Shanmugam, Lakshmanan S A, P. Dhanasekaran, P. Mahalakshmi, A. Sharmila\",\"doi\":\"10.1109/i-PACT52855.2021.9696463\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The implicit message is usually brought to the spectators through activities involving various body parts like hands, face and arms. This is prominently known as Gesture and many such gestures are generally performed through hands in an involuntary manner. Smartness is to keep tabs on these hand gestures and derive purposeful details out of it. Convolutional neural networks (CNN) track these complex movements and help in extracting prime features. In this paper, training and testing were done consecutively with the aid of images to check the effectiveness of CNN and results are presented.\",\"PeriodicalId\":335956,\"journal\":{\"name\":\"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/i-PACT52855.2021.9696463\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Innovations in Power and Advanced Computing Technologies (i-PACT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/i-PACT52855.2021.9696463","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

隐含的信息通常是通过涉及手、脸和手臂等身体部位的活动传递给观众的。这就是众所周知的手势,许多这样的手势通常是通过手无意识地完成的。聪明是密切关注这些手势,并从中获得有目的的细节。卷积神经网络(CNN)跟踪这些复杂的运动,并帮助提取主要特征。本文借助图像连续进行训练和测试,验证了CNN的有效性,并给出了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Hand Gesture Recognition using Convolutional Neural Network
The implicit message is usually brought to the spectators through activities involving various body parts like hands, face and arms. This is prominently known as Gesture and many such gestures are generally performed through hands in an involuntary manner. Smartness is to keep tabs on these hand gestures and derive purposeful details out of it. Convolutional neural networks (CNN) track these complex movements and help in extracting prime features. In this paper, training and testing were done consecutively with the aid of images to check the effectiveness of CNN and results are presented.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Abnormality Detection in Humerus Bone Radiographs Using DenseNet Random Optimal Search Based Significant Gene Identification and Classification of Disease Samples Co-Design Approach of Converter Control for Battery Charging Electric Vehicle Applications Typical Analysis of Different Natural Esters and their Performance: A Review Machine Learning-Based Medium Access Control Protocol for Heterogeneous Wireless Networks: A Review
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1