File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Keyfilter-aware Real-time UAV Object Tracking

TitleKeyfilter-aware Real-time UAV Object Tracking
Authors
KeywordsUnmanned aerial vehicles
Correlation
Visualization
Object tracking
Frequency-domain analysis
Issue Date2020
PublisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000639
Citation
Proceedings of IEEE International Conference on Robotics and Automation (ICRA 2020), Virtually Conference, 31 May - 31 August 2020, p. 193-199 How to Cite?
AbstractCorrelation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) with high efficiency. However, it has two imperfections, i.e., boundary effect and filter corruption. Several methods enlarging the search area can mitigate boundary effect, yet introducing undesired background distraction. Existing frame-by-frame context learning strategies for repressing background distraction nevertheless lower the tracking speed. Inspired by keyframe-based simultaneous localization and mapping, keyfilter is proposed in visual tracking for the first time, in order to handle the above issues efficiently and effectively. Keyfilters generated by periodically selected keyframes learn the context intermittently and are used to restrain the learning of filters, so that 1) context awareness can be transmitted to all the filters via keyfilter restriction, and 2) filter corruption can be repressed. Compared to the state-of-the-art results, our tracker performs better on two challenging benchmarks, with enough speed for UAV real-time applications.
Persistent Identifierhttp://hdl.handle.net/10722/284882
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLi, Y-
dc.contributor.authorFu, C-
dc.contributor.authorHuang, Z-
dc.contributor.authorZhang, Y-
dc.contributor.authorPan, J-
dc.date.accessioned2020-08-07T09:03:52Z-
dc.date.available2020-08-07T09:03:52Z-
dc.date.issued2020-
dc.identifier.citationProceedings of IEEE International Conference on Robotics and Automation (ICRA 2020), Virtually Conference, 31 May - 31 August 2020, p. 193-199-
dc.identifier.issn1050-4729-
dc.identifier.urihttp://hdl.handle.net/10722/284882-
dc.description.abstractCorrelation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) with high efficiency. However, it has two imperfections, i.e., boundary effect and filter corruption. Several methods enlarging the search area can mitigate boundary effect, yet introducing undesired background distraction. Existing frame-by-frame context learning strategies for repressing background distraction nevertheless lower the tracking speed. Inspired by keyframe-based simultaneous localization and mapping, keyfilter is proposed in visual tracking for the first time, in order to handle the above issues efficiently and effectively. Keyfilters generated by periodically selected keyframes learn the context intermittently and are used to restrain the learning of filters, so that 1) context awareness can be transmitted to all the filters via keyfilter restriction, and 2) filter corruption can be repressed. Compared to the state-of-the-art results, our tracker performs better on two challenging benchmarks, with enough speed for UAV real-time applications.-
dc.languageeng-
dc.publisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000639-
dc.relation.ispartofIEEE International Conference on Robotics and Automation (ICRA)-
dc.rightsIEEE International Conference on Robotics and Automation (ICRA). Copyright © IEEE, Computer Society.-
dc.rights©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectUnmanned aerial vehicles-
dc.subjectCorrelation-
dc.subjectVisualization-
dc.subjectObject tracking-
dc.subjectFrequency-domain analysis-
dc.titleKeyfilter-aware Real-time UAV Object Tracking-
dc.typeConference_Paper-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.identifier.doi10.1109/ICRA40945.2020.9196943-
dc.identifier.scopuseid_2-s2.0-85092733362-
dc.identifier.hkuros312202-
dc.identifier.spage193-
dc.identifier.epage199-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats