Detection of Traffic Violations of Road Users Based on Convolutional Neural Networks

Jakub Špaňhel, Jakub Sochor, A. Makarov
{"title":"Detection of Traffic Violations of Road Users Based on Convolutional Neural Networks","authors":"Jakub Špaňhel, Jakub Sochor, A. Makarov","doi":"10.1109/NEUREL.2018.8586996","DOIUrl":null,"url":null,"abstract":"In this paper, we explore the implementation of vehicle and pedestrian detection based on neural networks in a real-world application. We suggest changes to the previously published method with respect to capabilities of low-powered devices, such as Nvidia Jetson platform. Our experimental evaluation shows that detectors are capable of running 10.7 FPS on Jetson TX2 and can be used in real-world applications.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2018.8586996","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

In this paper, we explore the implementation of vehicle and pedestrian detection based on neural networks in a real-world application. We suggest changes to the previously published method with respect to capabilities of low-powered devices, such as Nvidia Jetson platform. Our experimental evaluation shows that detectors are capable of running 10.7 FPS on Jetson TX2 and can be used in real-world applications.
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
基于卷积神经网络的道路使用者交通违法行为检测
在本文中,我们探讨了在现实应用中基于神经网络的车辆和行人检测的实现。我们建议针对低功耗设备(如Nvidia Jetson平台)的功能对先前发布的方法进行更改。我们的实验评估表明,检测器能够在Jetson TX2上运行10.7 FPS,可以在实际应用中使用。
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
Brain - Machine Interfaces in the Context of Artificial Intelligence Development Feature Selection for Image Distortion Classification Supervised and Unsupervised Learning of Fetal Heart Rate Tracings with Deep Gaussian Processes Modeling and Optimization of Hexavalent Chromium Sorption onto Amberjet 1200H by Using Multiple-Linear Regression Real-Time Multi-Sensor Infrared Imagery Enhancement
×
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