A method of counting pedestrians in crowded scenes
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Byeoung-Su | - |
dc.contributor.author | Lee, Gwang-Gook | - |
dc.contributor.author | Yoon, Ja-Young | - |
dc.contributor.author | Kim, Jae Jun | - |
dc.contributor.author | Kim, Whoi-Yul | - |
dc.date.accessioned | 2022-12-21T01:19:23Z | - |
dc.date.available | 2022-12-21T01:19:23Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2008-09 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/177936 | - |
dc.description.abstract | This paper proposes a method to automatically count the number of pedestrians in a video input of a crowed scene. The method proposed in this paper improves on our previous pedestrian counting method which estimates the number of pedestrians by accumulating low-level features (foreground pixels and motion vectors) on a virtual gate. To handle crowded scenes, the pedestrian counting process in this paper is weighted by the ratio of foreground pixels in the scene. The relationship between crowdedness and weighting factor is learned from 10,000 simulation images. Tests on real video sequences show that this method can successfully estimate the number of pedestrians with an accuracy of about 95%. Also, when compared to the previous method, the accuracy was increased by about 5% for highly crowded scenes. Moreover, the proposed method runs at an average rate of around 60 fps on a standard PC, which makes the algorithm realistic for multi-camera systems. ? 2008 Springer-Verlag Berlin Heidelberg. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer | - |
dc.title | A method of counting pedestrians in crowded scenes | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Jae Jun | - |
dc.contributor.affiliatedAuthor | Kim, Whoi-Yul | - |
dc.identifier.doi | 10.1007/978-3-540-85984-0_134 | - |
dc.identifier.scopusid | 2-s2.0-53049085226 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.5227 LNAI, pp.1117 - 1126 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 5227 LNAI | - |
dc.citation.startPage | 1117 | - |
dc.citation.endPage | 1126 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Artificial intelligence | - |
dc.subject.keywordPlus | Estimation | - |
dc.subject.keywordPlus | Imaging techniques | - |
dc.subject.keywordPlus | Photography | - |
dc.subject.keywordPlus | Standards | - |
dc.subject.keywordPlus | Video recording | - |
dc.subject.keywordPlus | Average rate | - |
dc.subject.keywordPlus | Counting processes | - |
dc.subject.keywordPlus | Intelligent computing | - |
dc.subject.keywordPlus | International conferences | - |
dc.subject.keywordPlus | Low-level features | - |
dc.subject.keywordPlus | Motion Vectors | - |
dc.subject.keywordPlus | Multi-camera systems | - |
dc.subject.keywordPlus | Pedestrian flow | - |
dc.subject.keywordPlus | People counting | - |
dc.subject.keywordPlus | Real video sequences | - |
dc.subject.keywordPlus | Virtual gate | - |
dc.subject.keywordPlus | Visual surveillance | - |
dc.subject.keywordPlus | Weighting factors | - |
dc.subject.keywordPlus | Pixels | - |
dc.subject.keywordAuthor | Pedestrian flow | - |
dc.subject.keywordAuthor | People counting | - |
dc.subject.keywordAuthor | Visual surveillance | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-540-85984-0_134 | - |
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