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Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV
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, Department of Electrical Engineering, Computer Vision.
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2016 (English)In: Proceedings of the 12th International Symposium on Advances in Visual Computing, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Visual object tracking performance has improved significantly in recent years. Most trackers are based on either of two paradigms: online learning of an appearance model or the use of a pre-trained object detector. Methods based on online learning provide high accuracy, but are prone to model drift. The model drift occurs when the tracker fails to correctly estimate the tracked object’s position. Methods based on a detector on the other hand typically have good long-term robustness, but reduced accuracy compared to online methods.

Despite the complementarity of the aforementioned approaches, the problem of fusing them into a single framework is largely unexplored. In this paper, we propose a novel fusion between an online tracker and a pre-trained detector for tracking humans from a UAV. The system operates at real-time on a UAV platform. In addition we present a novel dataset for long-term tracking in a UAV setting, that includes scenarios that are typically not well represented in standard visual tracking datasets.

Place, publisher, year, edition, pages
2016.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-137897DOI: 10.1007/978-3-319-50835-1_50Scopus ID: 2-s2.0-85007039301ISBN: 978-3-319-50834-4 (print)ISBN: 978-3-319-50835-1 (electronic)OAI: oai:DiVA.org:liu-137897DiVA: diva2:1104310
Conference
International Symposium on Advances in Visual Computing
Available from: 2017-05-31 Created: 2017-05-31 Last updated: 2017-06-15Bibliographically approved

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Häger, GustavBhat, GoutamDanelljan, MartinKhan, Fahad ShahbazFelsberg, MichaelRudol, PiotrDoherty, Patrick
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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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