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Accurate Scale Estimation for Robust Visual Tracking
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-6096-3648
2014 (English)In: Proceedings of the British Machine Vision Conference 2014 / [ed] Michel Valstar, Andrew French and Tony Pridmore, BMVA Press , 2014Conference paper, Published paper (Refereed)
Abstract [en]

Robust scale estimation is a challenging problem in visual object tracking. Most existing methods fail to handle large scale variations in complex image sequences. This paper presents a novel approach for robust scale estimation in a tracking-by-detection framework. The proposed approach works by learning discriminative correlation filters based on a scale pyramid representation. We learn separate filters for translation and scale estimation, and show that this improves the performance compared to an exhaustive scale search. Our scale estimation approach is generic as it can be incorporated into any tracking method with no inherent scale estimation.

Experiments are performed on 28 benchmark sequences with significant scale variations. Our results show that the proposed approach significantly improves the performance by 18.8 % in median distance precision compared to our baseline. Finally, we provide both quantitative and qualitative comparison of our approach with state-of-the-art trackers in literature. The proposed method is shown to outperform the best existing tracker by 16.6 % in median distance precision, while operating at real-time.

Place, publisher, year, edition, pages
BMVA Press , 2014.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-113948DOI: 10.5244/C.28.65ISBN: 1901725529 (print)OAI: oai:DiVA.org:liu-113948DiVA: diva2:785778
Conference
British Machine Vision Conference, Nottingham, September 1-5, 2014
Available from: 2015-02-03 Created: 2015-02-03 Last updated: 2016-06-08Bibliographically approved

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Danelljan, MartinHäger, GustavKhan, FahadFelsberg, Michael
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