Occluded Pedestrian-Attribute Recognition for Video Sensors Using Group Sparsity
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Geonu | - |
dc.contributor.author | Yun, Kimin | - |
dc.contributor.author | Cho, Jungchan | - |
dc.date.accessioned | 2022-10-12T06:40:08Z | - |
dc.date.available | 2022-10-12T06:40:08Z | - |
dc.date.created | 2022-09-22 | - |
dc.date.issued | 2022-09 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85652 | - |
dc.description.abstract | Pedestrians are often obstructed by other objects or people in real-world vision sensors. These obstacles make pedestrian-attribute recognition (PAR) difficult; hence, occlusion processing for visual sensing is a key issue in PAR. To address this problem, we first formulate the identification of non-occluded frames as temporal attention based on the sparsity of a crowded video. In other words, a model for PAR is guided to prevent paying attention to the occluded frame. However, we deduced that this approach cannot include a correlation between attributes when occlusion occurs. For example, "boots" and "shoe color" cannot be recognized simultaneously when the foot is invisible. To address the uncorrelated attention issue, we propose a novel temporal-attention module based on group sparsity. Group sparsity is applied across attention weights in correlated attributes. Accordingly, physically-adjacent pedestrian attributes are grouped, and the attention weights of a group are forced to focus on the same frames. Experimental results indicate that the proposed method achieved 1.18% and 6.21% higher F-1-scores than the advanced baseline method on the occlusion samples in DukeMTMC-VideoRelD and MARS video-based PAR datasets, respectively. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | SENSORS | - |
dc.title | Occluded Pedestrian-Attribute Recognition for Video Sensors Using Group Sparsity | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000851799600001 | - |
dc.identifier.doi | 10.3390/s22176626 | - |
dc.identifier.bibliographicCitation | SENSORS, v.22, no.17 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85137590077 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 17 | - |
dc.contributor.affiliatedAuthor | Lee, Geonu | - |
dc.contributor.affiliatedAuthor | Cho, Jungchan | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | group-sparsity loss | - |
dc.subject.keywordAuthor | temporal attention module | - |
dc.subject.keywordAuthor | video-based pedestrianattribute recognition | - |
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.