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Conference Paper: Augmented Memory For Correlation Filters In Real-time Uav Tracking

TitleAugmented Memory For Correlation Filters In Real-time Uav Tracking
Authors
KeywordsAerial Systems
Perception and Autonomy
Computer Vision for Automation
Intelligent robots
Computational efficiency
Issue Date2020
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000393
Citation
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Consumer Robots and Our Future, Virtual Conference, Las Vegas, USA, 25 October 2020 - 24 January 2021, p. 1559-1566 How to Cite?
AbstractThe outstanding computational efficiency of discriminative correlation filter (DCF) fades away with various complicated improvements. Previous appearances are also gradually forgotten due to the exponential decay of historical views in traditional appearance updating scheme of DCF framework, reducing the model’s robustness. In this work, a novel tracker based on DCF framework is proposed to augment memory of previously appeared views while running at real-time speed. Several historical views and the current view are simultaneously introduced in training to allow the tracker to adapt to new appearances as well as memorize previous ones. A novel rapid compressed context learning is proposed to increase the discriminative ability of the filter efficiently. Substantial experiments on UAVDT and UAV123 datasets have validated that the proposed tracker performs competitively against other 26 top DCF and deep-based trackers with over 40fps on CPU.
DescriptionMoAT20 Aerial Systems: Perception - Paper MoAT20.3
Persistent Identifierhttp://hdl.handle.net/10722/284881
ISSN
2023 SCImago Journal Rankings: 1.094
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Y-
dc.contributor.authorFu, C-
dc.contributor.authorDing, F-
dc.contributor.authorHuang, Z-
dc.contributor.authorPan, J-
dc.date.accessioned2020-08-07T09:03:51Z-
dc.date.available2020-08-07T09:03:51Z-
dc.date.issued2020-
dc.identifier.citationIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Consumer Robots and Our Future, Virtual Conference, Las Vegas, USA, 25 October 2020 - 24 January 2021, p. 1559-1566-
dc.identifier.issn2153-0858-
dc.identifier.urihttp://hdl.handle.net/10722/284881-
dc.descriptionMoAT20 Aerial Systems: Perception - Paper MoAT20.3-
dc.description.abstractThe outstanding computational efficiency of discriminative correlation filter (DCF) fades away with various complicated improvements. Previous appearances are also gradually forgotten due to the exponential decay of historical views in traditional appearance updating scheme of DCF framework, reducing the model’s robustness. In this work, a novel tracker based on DCF framework is proposed to augment memory of previously appeared views while running at real-time speed. Several historical views and the current view are simultaneously introduced in training to allow the tracker to adapt to new appearances as well as memorize previous ones. A novel rapid compressed context learning is proposed to increase the discriminative ability of the filter efficiently. Substantial experiments on UAVDT and UAV123 datasets have validated that the proposed tracker performs competitively against other 26 top DCF and deep-based trackers with over 40fps on CPU.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000393-
dc.relation.ispartofIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Proceedings-
dc.subjectAerial Systems-
dc.subjectPerception and Autonomy-
dc.subjectComputer Vision for Automation-
dc.subjectIntelligent robots-
dc.subjectComputational efficiency-
dc.titleAugmented Memory For Correlation Filters In Real-time Uav Tracking-
dc.typeConference_Paper-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/IROS45743.2020.9341595-
dc.identifier.scopuseid_2-s2.0-85098438144-
dc.identifier.hkuros312165-
dc.identifier.spage1559-
dc.identifier.epage1566-
dc.identifier.isiWOS:000714033800039-
dc.publisher.placeUnited States-
dc.identifier.issnl2153-0858-

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