Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in Tashkent
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kutlimuratov Alpamis Jaksymuratovich | - |
dc.contributor.author | Khamzaev, Jamshid | - |
dc.contributor.author | Kuchkorov, Temur | - |
dc.contributor.author | Anwar, Muhammad Shahid | - |
dc.contributor.author | Choi, Ahyoung | - |
dc.date.accessioned | 2023-06-30T14:40:52Z | - |
dc.date.available | 2023-06-30T14:40:52Z | - |
dc.date.issued | 2023-05 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/88316 | - |
dc.description.abstract | This study describes an applied and enhanced real-time vehicle-counting system that is an integral part of intelligent transportation systems. The primary objective of this study was to develop an accurate and reliable real-time system for vehicle counting to mitigate traffic congestion in a designated area. The proposed system can identify and track objects inside the region of interest and count detected vehicles. To enhance the accuracy of the system, we used the You Only Look Once version 5 (YOLOv5) model for vehicle identification owing to its high performance and short computing time. Vehicle tracking and the number of vehicles acquired used the DeepSort algorithm with the Kalman filter and Mahalanobis distance as the main components of the algorithm and the proposed simulated loop technique, respectively. Empirical results were obtained using video images taken from a closed-circuit television (CCTV) camera on Tashkent roads and show that the counting system can produce 98.1% accuracy in 0.2408 s. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in Tashkent | - |
dc.type | Article | - |
dc.identifier.wosid | 001005204400001 | - |
dc.identifier.doi | 10.3390/s23115007 | - |
dc.identifier.bibliographicCitation | SENSORS, v.23, no.11 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85161519457 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 23 | - |
dc.citation.number | 11 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | vehicle counting | - |
dc.subject.keywordAuthor | YOLOv5 | - |
dc.subject.keywordAuthor | intelligent transportation system | - |
dc.subject.keywordAuthor | smart city | - |
dc.subject.keywordPlus | VEHICLE DETECTION | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.