Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices

Anish R. Khadka, V. Argyriou, Paolo Remagnino
{"title":"Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices","authors":"Anish R. Khadka, V. Argyriou, Paolo Remagnino","doi":"10.1109/DCOSS49796.2020.00049","DOIUrl":null,"url":null,"abstract":"A novel deep neural network is proposed, for accurate and robust crowd counting. Crowd counting is a complex task, as it strongly depends on the deployed camera characteristics and, above all, the scene perspective. Crowd counting is essential in security applications where Internet of Things (IoT) cameras are deployed to help with crowd management tasks. The complexity of a scene varies greatly, and a medium to large scale security system based on IoT cameras must cater for changes in perspective and how people appear from different vantage points. To address this, our deep architecture extracts multi-scale features with a pyramid contextual module to provide long-range contextual information and enlarge the receptive field. Experiments were run on three major crowd counting datasets, to test our proposed method. Results demonstrate our method supersedes the performance of state-of-the-art methods.","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCOSS49796.2020.00049","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

A novel deep neural network is proposed, for accurate and robust crowd counting. Crowd counting is a complex task, as it strongly depends on the deployed camera characteristics and, above all, the scene perspective. Crowd counting is essential in security applications where Internet of Things (IoT) cameras are deployed to help with crowd management tasks. The complexity of a scene varies greatly, and a medium to large scale security system based on IoT cameras must cater for changes in perspective and how people appear from different vantage points. To address this, our deep architecture extracts multi-scale features with a pyramid contextual module to provide long-range contextual information and enlarge the receptive field. Experiments were run on three major crowd counting datasets, to test our proposed method. Results demonstrate our method supersedes the performance of state-of-the-art methods.
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
面向智能物联网视频采集设备的精准深度网络人群统计
提出了一种新颖的深度神经网络,用于准确和鲁棒的人群计数。人群计数是一项复杂的任务,因为它强烈依赖于部署的摄像机特性,最重要的是,场景视角。在部署物联网(IoT)摄像头以帮助进行人群管理任务的安全应用中,人群计数是必不可少的。场景的复杂性变化很大,基于物联网摄像头的中型到大型安全系统必须满足视角的变化以及人们从不同有利位置出现的方式。为了解决这个问题,我们的深层架构使用金字塔上下文模块提取多尺度特征,以提供远程上下文信息并扩大接受野。实验在三个主要的人群计数数据集上运行,以测试我们提出的方法。结果表明,我们的方法取代了最先进的方法的性能。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Health Information Exchange with Blockchain amid Covid-19-like Pandemics Instrumentation for Cooking Pattern Analysis in Peri-Urban Nepal Predictive and Explainable Machine Learning for Industrial Internet of Things Applications Message from the IoTI4 2020 Workshop Chairs An Agnostic Data-Driven Approach to Predict Stoppages of Industrial Packing Machine in Near
×
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