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DCCO: Towards Deformable Continuous Convolution Operators 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
2017 (English)In: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, Vol. 10424, p. 55-67Conference paper, Published paper (Refereed)
Abstract [en]

Discriminative Correlation Filter (DCF) based methods have shown competitive performance on tracking benchmarks in recent years. Generally, DCF based trackers learn a rigid appearance model of the target. However, this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations. In this paper, we propose a unified formulation for learning a deformable convolution filter. In our framework, the deformable filter is represented as a linear combination of sub-filters. Both the sub-filter coefficients and their relative locations are inferred jointly in our formulation. Experiments are performed on three challenging tracking benchmarks: OTB-2015, TempleColor and VOT2016. Our approach improves the baseline method, leading to performance comparable to state-of-the-art.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10424, p. 55-67
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-145373DOI: 10.1007/978-3-319-64689-3_5ISI: 000432085900005ISBN: 9783319646886 (print)ISBN: 9783319646893 (electronic)OAI: oai:DiVA.org:liu-145373DiVA, id: diva2:1185623
Conference
17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I
Note

Funding agencies: SSF (SymbiCloud); VR (EMC2) [2016-05543]; SNIC; WASP; Nvidia

Available from: 2018-02-26 Created: 2018-02-26 Last updated: 2018-06-01Bibliographically approved

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Johnander, JoakimDanelljan, MartinKhan, Fahad ShahbazFelsberg, Michael

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