Motion-aware ensemble of three-mode trackers for unmanned aerial vehicles
DC Field | Value | Language |
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dc.contributor.author | Lee, Kyuewang | - |
dc.contributor.author | Chang, Hyung Jin | - |
dc.contributor.author | Choi, Jongwon | - |
dc.contributor.author | Heo, Byeongho | - |
dc.contributor.author | Leonardis, Ales | - |
dc.contributor.author | Choi, Jin Young | - |
dc.date.accessioned | 2021-06-18T07:14:15Z | - |
dc.date.available | 2021-06-18T07:14:15Z | - |
dc.date.issued | 2021-05 | - |
dc.identifier.issn | 0932-8092 | - |
dc.identifier.issn | 1432-1769 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44120 | - |
dc.description.abstract | To tackle problems arising from unexpected camera motions in unmanned aerial vehicles (UAVs), we propose a three-mode ensemble tracker where each mode specializes in distinctive situations. The proposed ensemble tracker is composed of appearance-based tracking mode, homography-based tracking mode, and momentum-based tracking mode. The appearance-based tracking mode tracks a moving object well when the UAV is nearly stopped, whereas the homography-based tracking mode shows good tracking performance under smooth UAV or object motion. The momentum-based tracking mode copes with large or abrupt motion of either the UAV or the object. We evaluate the proposed tracking scheme on a widely-used UAV123 benchmark dataset. The proposed motion-aware ensemble shows a 5.3% improvement in average precision compared to the baseline correlation filter tracker, which effectively employs deep features while achieving a tracking speed of at least 80fps in our experimental settings. In addition, the proposed method outperforms existing real-time correlation filter trackers. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER | - |
dc.title | Motion-aware ensemble of three-mode trackers for unmanned aerial vehicles | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s00138-021-01181-x | - |
dc.identifier.bibliographicCitation | MACHINE VISION AND APPLICATIONS, v.32, no.3 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000625832500001 | - |
dc.identifier.scopusid | 2-s2.0-85102116558 | - |
dc.citation.number | 3 | - |
dc.citation.title | MACHINE VISION AND APPLICATIONS | - |
dc.citation.volume | 32 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Visual tracking | - |
dc.subject.keywordAuthor | Correlation filter tracking | - |
dc.subject.keywordAuthor | Motion-aware ensemble method | - |
dc.subject.keywordAuthor | Unmanned surveillance vehicles | - |
dc.subject.keywordPlus | TRACKING | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Cybernetics | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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