Real-time pedestrian detection using support vector machines
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, SH | - |
dc.contributor.author | Byun, H | - |
dc.contributor.author | Lee, SW | - |
dc.date.accessioned | 2021-09-09T12:27:03Z | - |
dc.date.available | 2021-09-09T12:27:03Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2003-05 | - |
dc.identifier.issn | 0218-0014 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/124358 | - |
dc.description.abstract | In this paper, we present a real-time pedestrian detection method in outdoor environments. It is necessary for pedestrian detection to implement obstacle and face detection which are major parts of a walking guidance system for the visually impaired. It detects foreground objects on the ground, discriminates pedestrians from other noninterest objects, and extracts candidate regions for face detection and recognition. For effective real-time pedestrian detection, we have developed a method using stereo-based segmentation and the SVM (Support Vector Machines), which works well particularly in binary classification problem (e.g. object detection). We used vertical edge features extracted from arms, legs and torso. In our experiments, test results on a large number of outdoor scenes demonstrated the effectiveness of the proposed pedestrian detection method. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WORLD SCIENTIFIC PUBL CO PTE LTD | - |
dc.subject | TRACKING | - |
dc.title | Real-time pedestrian detection using support vector machines | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, SW | - |
dc.identifier.doi | 10.1142/S0218001403002435 | - |
dc.identifier.wosid | 000183574300006 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.17, no.3, pp.405 - 416 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | - |
dc.citation.title | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | - |
dc.citation.volume | 17 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 405 | - |
dc.citation.endPage | 416 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | TRACKING | - |
dc.subject.keywordAuthor | pedestrian detection | - |
dc.subject.keywordAuthor | support vector machines | - |
dc.subject.keywordAuthor | stereo vision | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea 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.