Mutual Learning Networks Of Actor Relation Graph For Group Activity Recognition

Zhu Ya Lou, L. Fan, Kuang Ping, Feng Dong
{"title":"Mutual Learning Networks Of Actor Relation Graph For Group Activity Recognition","authors":"Zhu Ya Lou, L. Fan, Kuang Ping, Feng Dong","doi":"10.1109/ICCWAMTIP53232.2021.9674170","DOIUrl":null,"url":null,"abstract":"The Actor Relation Graph (ARG) is an effective method for detecting group behaviour but still needs improvement in some areas. In this paper, we propose using the sum of absolute differences (SAD) to compute the similarity of characters' appearance, introduce deep mutual learning to support the network's training, and add a visualization model. By training with the extended dataset, the results show that our improved network can achieve the expected better prediction accuracy of group activities.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674170","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The Actor Relation Graph (ARG) is an effective method for detecting group behaviour but still needs improvement in some areas. In this paper, we propose using the sum of absolute differences (SAD) to compute the similarity of characters' appearance, introduce deep mutual learning to support the network's training, and add a visualization model. By training with the extended dataset, the results show that our improved network can achieve the expected better prediction accuracy of group activities.
面向群体活动识别的行动者关系图互学习网络
行动者关系图(ARG)是一种有效的群体行为检测方法,但在某些领域仍有待改进。在本文中,我们提出使用绝对差和(SAD)来计算字符外观的相似性,引入深度相互学习来支持网络的训练,并添加可视化模型。通过对扩展数据集的训练,结果表明改进后的网络可以达到预期的较好的群体活动预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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