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On the Optimization of Advanced DCF-Trackers
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.
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2018 (English)In: Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part I / [ed] Laura Leal-TaixéStefan Roth, Cham: Springer Publishing Company, 2018, p. 54-69Conference paper, Published paper (Refereed)
Abstract [en]

Trackers based on discriminative correlation filters (DCF) have recently seen widespread success and in this work we dive into their numerical core. DCF-based trackers interleave learning of the target detector and target state inference based on this detector. Whereas the original formulation includes a closed-form solution for the filter learning, recently introduced improvements to the framework no longer have known closed-form solutions. Instead a large-scale linear least squares problem must be solved each time the detector is updated. We analyze the procedure used to optimize the detector and let the popular scheme introduced with ECO serve as a baseline. The ECO implementation is revisited in detail and several mechanisms are provided with alternatives. With comprehensive experiments we show which configurations are superior in terms of tracking capabilities and optimization performance.

Place, publisher, year, edition, pages
Cham: Springer Publishing Company, 2018. p. 54-69
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11129
National Category
Engineering and Technology Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-161036DOI: 10.1007/978-3-030-11009-3_2ISBN: 9783030110086 (print)ISBN: 9783030110093 (electronic)OAI: oai:DiVA.org:liu-161036DiVA, id: diva2:1361993
Conference
Conference on Computer Vision (ECCV) Workshops, Munich, Germany, 8-14 September, 2018
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-10-30Bibliographically approved

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On the Optimization of Advanced DCF-Trackers(386 kB)12 downloads
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