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Learning Spatially Regularized Correlation Filters for Visual Tracking
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
2015 (English)In: Proceedings of the International Conference in Computer Vision (ICCV), 2015, IEEE Computer Society, 2015, 4310-4318 p.Conference paper, Published paper (Refereed)
Abstract [en]

Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. Recently, discriminatively learned correlation filters (DCF) have been successfully applied to address this problem for tracking. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood. However, the periodic assumption also introduces unwanted boundary effects, which severely degrade the quality of the tracking model.

We propose Spatially Regularized Discriminative Correlation Filters (SRDCF) for tracking. A spatial regularization component is introduced in the learning to penalize correlation filter coefficients depending on their spatial location. Our SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples. We further propose an optimization strategy, based on the iterative Gauss-Seidel method, for efficient online learning of our SRDCF. Experiments are performed on four benchmark datasets: OTB-2013, ALOV++, OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and 8.2% respectively, in mean overlap precision, compared to the best existing trackers.

Place, publisher, year, edition, pages
IEEE Computer Society, 2015. 4310-4318 p.
Series
IEEE International Conference on Computer Vision. Proceedings, ISSN 1550-5499
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-121609DOI: 10.1109/ICCV.2015.490ISI: 000380414100482ISBN: 978-1-4673-8390-5 (print)OAI: oai:DiVA.org:liu-121609DiVA: diva2:857265
Conference
International Conference in Computer Vision (ICCV), Santiago, Chile, December 13-16, 2015
Available from: 2015-09-28 Created: 2015-09-28 Last updated: 2016-09-19

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Danelljan, MartinHäger, GustavKhan, Fahad ShahbazFelsberg, Michael

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CiteExportLink to record
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  • apa
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