Enhanced Visual Object Tracking and Segmentation in Military Environments: Overcoming Camouflage and Deformation Challenges
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Injae | - |
dc.contributor.author | Lee, Sanga | - |
dc.contributor.author | Kim, Jinseop | - |
dc.contributor.author | Choi, Hyeonjoon | - |
dc.contributor.author | Park, Sinyoung | - |
dc.contributor.author | Paik, Joonki | - |
dc.date.accessioned | 2024-04-19T06:30:26Z | - |
dc.date.available | 2024-04-19T06:30:26Z | - |
dc.date.issued | 2024-01 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/73333 | - |
dc.description.abstract | Visual object tracking is a critical component of border surveillance technology, complementing object detection in its importance. Despite significant advancements enhancing tracking performance, challenges such as object drifting and discrimination among similar objects persist. This is particularly problematic in military settings where distinguishing between soldiers with matching attire is arduous. This paper introduces an innovative model capable of executing visual object tracking and segmentation in tandem. The model's update mechanism allows for sustained tracking, adeptly handling significant variances in the initial bounding box. Enhanced tracking of camouflaged soldiers was achieved through the incorporation of specialized learning datasets focused on camouflage patterns. Testing our model on both standard benchmarks and tailored military datasets yielded impressive results, affirming the model's efficacy. © 2024 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Enhanced Visual Object Tracking and Segmentation in Military Environments: Overcoming Camouflage and Deformation Challenges | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICEIC61013.2024.10457247 | - |
dc.identifier.bibliographicCitation | 2024 International Conference on Electronics, Information, and Communication, ICEIC 2024 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85189243030 | - |
dc.citation.title | 2024 International Conference on Electronics, Information, and Communication, ICEIC 2024 | - |
dc.type.docType | Conference paper | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Video object segmentation | - |
dc.subject.keywordAuthor | Visual object tracking | - |
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.