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Adaptive Color Attributes for Real-Time 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.ORCID iD: 0000-0002-6096-3648
Computer Vision Center, CS Dept. Universitat Autonoma de Barcelona, Spain.
2014 (English)In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014, IEEE Computer Society, 2014, 1090-1097 p.Conference paper, Published paper (Refereed)
Abstract [en]

Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.

This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014. 1090-1097 p.
Series
IEEE Conference on Computer Vision and Pattern Recognition. Proceedings, ISSN 1063-6919
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-105857DOI: 10.1109/CVPR.2014.143Scopus ID: 2-s2.0-84911362613ISBN: 978-147995117-8 (print)OAI: oai:DiVA.org:liu-105857DiVA: diva2:711538
Conference
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 24-27, 2014
Note

Publication status: Accepted

Available from: 2014-04-10 Created: 2014-04-10 Last updated: 2016-05-04Bibliographically approved

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Danelljan, MartinShahbaz Khan, FahadFelsberg, Michael

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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Output format
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