Robust Parking Occupancy Monitoring System Using Random Forests
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Woon | - |
dc.contributor.author | Park, Seokmok | - |
dc.contributor.author | Kim, Min-jae | - |
dc.contributor.author | Han, Sangpil | - |
dc.contributor.author | Kim, Minseo | - |
dc.contributor.author | Kim, Taewoo | - |
dc.contributor.author | Kim, Jaewoong | - |
dc.contributor.author | Paik, Joonki | - |
dc.date.accessioned | 2022-04-11T10:40:25Z | - |
dc.date.available | 2022-04-11T10:40:25Z | - |
dc.date.issued | 2018-01 | - |
dc.identifier.issn | 2377-8431 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/56206 | - |
dc.description.abstract | In recent years, with the growing number of vehicles, the efficient parking management system has become necessary for large buildings. This paper presents a parking occupancy monitoring system which can automatically decide whether a vehicle is parked or empty in each parking space. There are many obstacles in the parking area, such as high diversity in car models, occlusion by other car, moving person, waste trash, and camera lens distortion, making it difficult to detect a vehicle. In order to solve all these problems, this paper proposes to use a part-based and machine learning-based vehicle detection algorithm. We demonstrate that the proposed method performs well on large indoor parking lot dataset which contains the abovementioned obstacles. | - |
dc.format.extent | 4 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE | - |
dc.title | Robust Parking Occupancy Monitoring System Using Random Forests | - |
dc.type | Article | - |
dc.identifier.doi | 10.23919/ELINFOCOM.2018.8330608 | - |
dc.identifier.bibliographicCitation | 2018 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC), v.2018-January, pp 359 - 362 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000450093300109 | - |
dc.identifier.scopusid | 2-s2.0-85048594801 | - |
dc.citation.endPage | 362 | - |
dc.citation.startPage | 359 | - |
dc.citation.title | 2018 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC) | - |
dc.citation.volume | 2018-January | - |
dc.type.docType | Proceedings Paper | - |
dc.subject.keywordAuthor | Parking occupancy monitoring system | - |
dc.subject.keywordAuthor | indoor parking lot | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | part-based model | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang 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.