Improved vanishing point reference detection to early detect and track distant oncoming vehicles for adaptive traffic light signaling

Yoanda Alim Syahbana, D. Zulherman, Y. Yokota
{"title":"Improved vanishing point reference detection to early detect and track distant oncoming vehicles for adaptive traffic light signaling","authors":"Yoanda Alim Syahbana, D. Zulherman, Y. Yokota","doi":"10.20895/infotel.v15i2.890","DOIUrl":null,"url":null,"abstract":"Real-time traffic monitoring is essential for the operation of an adaptive traffic lighting system and plays a significant role in decision-making, particularly signaling in roadworks. When only one lane is accessible due to temporary road blockage, early detection of oncoming vehicles is crucial to minimize bottlenecks near the traffic light that could result in congestion and accidents. This research aimed to enhance the detection and tracking of traffic at a distance from the traffic light. We utilized the vanishing point as a reference for detection and calculated the region of interest. We implemented the proposed method on twelve traffic surveillance videos and evaluated the system performance based on how quickly it could detect incoming traffic compared with the R-CNN method. The proposed method detected target vehicles in an average of 17.75 frames, while the R-CNN method required an average of 63.36 frames. Moreover, the proposed method’s precision depends on the number of pixel orientations used to estimate the vanishing point and the definition of the region of interest. Therefore, the proposed method for enhancing the safety and reliability of an adaptive traffic light system is reliable.","PeriodicalId":30672,"journal":{"name":"Jurnal Infotel","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Infotel","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20895/infotel.v15i2.890","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

Real-time traffic monitoring is essential for the operation of an adaptive traffic lighting system and plays a significant role in decision-making, particularly signaling in roadworks. When only one lane is accessible due to temporary road blockage, early detection of oncoming vehicles is crucial to minimize bottlenecks near the traffic light that could result in congestion and accidents. This research aimed to enhance the detection and tracking of traffic at a distance from the traffic light. We utilized the vanishing point as a reference for detection and calculated the region of interest. We implemented the proposed method on twelve traffic surveillance videos and evaluated the system performance based on how quickly it could detect incoming traffic compared with the R-CNN method. The proposed method detected target vehicles in an average of 17.75 frames, while the R-CNN method required an average of 63.36 frames. Moreover, the proposed method’s precision depends on the number of pixel orientations used to estimate the vanishing point and the definition of the region of interest. Therefore, the proposed method for enhancing the safety and reliability of an adaptive traffic light system is reliable.
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
改进的消失点参考检测,用于自适应红绿灯信号的早期检测和跟踪远处迎面而来的车辆
实时交通监控对于自适应交通照明系统的运行至关重要,并在决策中发挥着重要作用,尤其是在道路工程中发出信号。当由于临时道路堵塞,只有一条车道可以通行时,尽早发现迎面而来的车辆对于最大限度地减少红绿灯附近可能导致拥堵和事故的瓶颈至关重要。这项研究旨在增强对交通信号灯远处交通的检测和跟踪。我们利用消失点作为检测的参考,并计算出感兴趣的区域。我们在12个交通监控视频上实现了所提出的方法,并根据与R-CNN方法相比,该方法检测传入交通的速度来评估系统性能。所提出的方法在平均17.75帧中检测到目标车辆,而R-CNN方法需要平均63.36帧。此外,所提出的方法的精度取决于用于估计消失点的像素方向的数量和感兴趣区域的定义。因此,所提出的提高自适应红绿灯系统安全性和可靠性的方法是可靠的。
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
47
审稿时长
6 weeks
期刊最新文献
Geo-Navigation in Museums: Augmented Reality Application in the Geological Museum Indonesia Cloud-based Metabase GIS Data Analysis Platform Quality Management According to ISO 9126 Indicators Solar Panel Power Generator with Automatic Charging using PWM System based on Microcontroller Weighted Voting Ensemble Learning of CNN Architectures for Diabetic Retinopathy Classification An Evaluation of Wireless Network Security with Penetration Testing Method at PT PLN UP2D S2JB
×
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