Context-Aware Correlation Filter Tracking

Handle URI:
http://hdl.handle.net/10754/626827
Title:
Context-Aware Correlation Filter Tracking
Authors:
Mueller, Matthias; Smith, Neil; Ghanem, Bernard ( 0000-0002-5534-587X )
Abstract:
Correlation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small neighborhood. In this paper, we present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multi-dimensional features in the primal and dual domain. Extensive experiments demonstrate that this framework significantly improves the performance of many CF trackers with only a modest impact on frame rate.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Visual Computing Center (VCC)
Citation:
Mueller M, Smith N, Ghanem B (2017) Context-Aware Correlation Filter Tracking. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/cvpr.2017.152.
Publisher:
IEEE
Journal:
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Conference/Event name:
30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Issue Date:
9-Nov-2017
DOI:
10.1109/cvpr.2017.152
Type:
Conference Paper
Sponsors:
This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research through the VCC funding.
Additional Links:
http://ieeexplore.ieee.org/document/8099635/
Appears in Collections:
Conference Papers; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMueller, Matthiasen
dc.contributor.authorSmith, Neilen
dc.contributor.authorGhanem, Bernarden
dc.date.accessioned2018-01-16T13:37:03Z-
dc.date.available2018-01-16T13:37:03Z-
dc.date.issued2017-11-09en
dc.identifier.citationMueller M, Smith N, Ghanem B (2017) Context-Aware Correlation Filter Tracking. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/cvpr.2017.152.en
dc.identifier.doi10.1109/cvpr.2017.152en
dc.identifier.urihttp://hdl.handle.net/10754/626827-
dc.description.abstractCorrelation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small neighborhood. In this paper, we present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multi-dimensional features in the primal and dual domain. Extensive experiments demonstrate that this framework significantly improves the performance of many CF trackers with only a modest impact on frame rate.en
dc.description.sponsorshipThis work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research through the VCC funding.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/8099635/en
dc.titleContext-Aware Correlation Filter Trackingen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.identifier.journal2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)en
dc.conference.dateJUL 21-26, 2017en
dc.conference.name30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)en
dc.conference.locationHonolulu, HIen
kaust.authorMueller, Matthiasen
kaust.authorSmith, Neilen
kaust.authorGhanem, Bernarden
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