利用 VGG19 进行人体步态识别的迁移学习:CASIA-A 数据集

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Veenu Rani, Munish Kumar
{"title":"利用 VGG19 进行人体步态识别的迁移学习:CASIA-A 数据集","authors":"Veenu Rani, Munish Kumar","doi":"10.1007/s11042-024-20132-y","DOIUrl":null,"url":null,"abstract":"<p>Identification of individuals based on physical characteristics has recently gained popularity and falls under the category of pattern recognition. Biometric recognition has emerged as an effective strategy for preventing security breaches, as no two people share the same physical characteristics. \"Gait recognition\" specifically refers to identifying individuals based on their walking patterns. Human gait is a method of locomotion that relies on the coordination of the brain, nerves, and muscles. Traditionally, human gait analysis was performed subjectively through visual observations. However, with advancements in technology and deep learning, human gait analysis can now be conducted empirically and without the need for subject cooperation, enhancing the quality of life. Deep learning methods have demonstrated excellent performance in human gait recognition. In this article, the authors employed the VGG19 transfer learning model for human gait recognition. They used the public benchmark dataset CASIA-A for their experimental work, which contains a total of 19,139 images captured from 20 individuals. The dataset was segmented into two different patterns: 70:30 and 80:20. To optimize the performance of the proposed model, the authors considered three hyperparameters: loss, validation loss (val_loss), and accuracy rate. They reported accuracy rates of 96.9% and 97.8%, with losses of 2.71% and 2.01% for the two patterns, respectively.</p>","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transfer learning for human gait recognition using VGG19: CASIA-A dataset\",\"authors\":\"Veenu Rani, Munish Kumar\",\"doi\":\"10.1007/s11042-024-20132-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Identification of individuals based on physical characteristics has recently gained popularity and falls under the category of pattern recognition. Biometric recognition has emerged as an effective strategy for preventing security breaches, as no two people share the same physical characteristics. \\\"Gait recognition\\\" specifically refers to identifying individuals based on their walking patterns. Human gait is a method of locomotion that relies on the coordination of the brain, nerves, and muscles. Traditionally, human gait analysis was performed subjectively through visual observations. However, with advancements in technology and deep learning, human gait analysis can now be conducted empirically and without the need for subject cooperation, enhancing the quality of life. Deep learning methods have demonstrated excellent performance in human gait recognition. In this article, the authors employed the VGG19 transfer learning model for human gait recognition. They used the public benchmark dataset CASIA-A for their experimental work, which contains a total of 19,139 images captured from 20 individuals. The dataset was segmented into two different patterns: 70:30 and 80:20. To optimize the performance of the proposed model, the authors considered three hyperparameters: loss, validation loss (val_loss), and accuracy rate. They reported accuracy rates of 96.9% and 97.8%, with losses of 2.71% and 2.01% for the two patterns, respectively.</p>\",\"PeriodicalId\":18770,\"journal\":{\"name\":\"Multimedia Tools and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Multimedia Tools and Applications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11042-024-20132-y\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multimedia Tools and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11042-024-20132-y","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

基于物理特征的个人身份识别最近越来越受欢迎,属于模式识别的范畴。由于没有两个人具有相同的身体特征,生物识别已成为防止安全漏洞的有效策略。"步态识别 "特指根据行走模式来识别个人。人类步态是一种依靠大脑、神经和肌肉协调的运动方式。传统上,人类步态分析是通过视觉观察主观进行的。然而,随着技术和深度学习的进步,人类步态分析现在可以通过经验来进行,而且不需要受试者的配合,从而提高了生活质量。深度学习方法在人类步态识别方面表现出色。在本文中,作者采用了 VGG19 转移学习模型进行人类步态识别。他们在实验工作中使用了公共基准数据集 CASIA-A,该数据集包含从 20 个人身上捕获的共计 19,139 张图像。该数据集被分割成两种不同的模式:70:30 和 80:20。为了优化所提模型的性能,作者考虑了三个超参数:损失、验证损失(val_loss)和准确率。他们报告说,两种模式的准确率分别为 96.9% 和 97.8%,损失分别为 2.71% 和 2.01%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Transfer learning for human gait recognition using VGG19: CASIA-A dataset

Transfer learning for human gait recognition using VGG19: CASIA-A dataset

Identification of individuals based on physical characteristics has recently gained popularity and falls under the category of pattern recognition. Biometric recognition has emerged as an effective strategy for preventing security breaches, as no two people share the same physical characteristics. "Gait recognition" specifically refers to identifying individuals based on their walking patterns. Human gait is a method of locomotion that relies on the coordination of the brain, nerves, and muscles. Traditionally, human gait analysis was performed subjectively through visual observations. However, with advancements in technology and deep learning, human gait analysis can now be conducted empirically and without the need for subject cooperation, enhancing the quality of life. Deep learning methods have demonstrated excellent performance in human gait recognition. In this article, the authors employed the VGG19 transfer learning model for human gait recognition. They used the public benchmark dataset CASIA-A for their experimental work, which contains a total of 19,139 images captured from 20 individuals. The dataset was segmented into two different patterns: 70:30 and 80:20. To optimize the performance of the proposed model, the authors considered three hyperparameters: loss, validation loss (val_loss), and accuracy rate. They reported accuracy rates of 96.9% and 97.8%, with losses of 2.71% and 2.01% for the two patterns, respectively.

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
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信