Martin Danelljan
Martin Danelljan
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Tracking the Known and the Unknown by Leveraging Semantic Information
BMVC 2019
“e propose a tracking framework that can exploit semantic information, without sacrificing the generic nature of the tracker.
Ardhendu Tripathi
,
Martin Danelljan
,
Luc Van Gool
,
Radu Timofte
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ATOM: Accurate Tracking by Overlap Maximization
CVPR 2019
Oral
Performing accurate bounding box estimation for generic visual tracking.
Martin Danelljan
,
Goutam Bhat
,
Fahad Shahbaz Khan
,
Michael Felsberg
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Code
Video
DOI
arXiv
A Generative Appearance Model for End-to-end Video Object Segmentation
CVPR 2019
Oral
A generative appearance module for end-to-end VOS.
Joakim Johnander
,
Martin Danelljan
,
Emil Brissman
,
Fahad Shahbaz Khan
,
Michael Felsberg
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Code
Video
DOI
arXiv
Unveiling the Power of Deep Tracking
ECCV 2019
How to better utilize deep features for correlation-based tracking.
Goutam Bhat
,
Joakim Johnander
,
Martin Danelljan
,
Fahad Shahbaz Khan
,
Michael Felsberg
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Code
DOI
arXiv
Code (Unofficial Python)
Density Adaptive Point Set Registration
CVPR 2018
Oral
Revisiting the foundations of probabilistic point cloud registration in order to tackle the key issue of sampling density variations.
Felix Järemo Lawin
,
Martin Danelljan
,
Fahad Shahbaz Khan
,
Per-Erik Forssén
,
Michael Felsberg
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Code
Video
DOI
arXiv
ECO: Efficient Convolution Operators for Tracking
CVPR 2017
Tackling the key causes behind the problems of computational complexity and over-fitting in correlation trackers.
Martin Danelljan
,
Goutam Bhat
,
Fahad Shahbaz Khan
,
Michael Felsberg
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Code
Poster
DOI
arXiv
Code (Unofficial Python)
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
ECCV 2016
Oral
A theoretical framework for discriminatively learning a convolution operator in the continuous spatial domain.
Martin Danelljan
,
Andreas Robinson
,
Fahad Shahbaz Khan
,
Michael Felsberg
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Code
Poster
Slides
Video
DOI
arXiv
Supplementary
Code (Unofficial Python)
A Probabilistic Framework for Color-Based Point Set Registration
CVPR 2016
A probabilistic point set registration framework that exploits available color information associated with the points.
Martin Danelljan
,
Giulia Meneghetti
,
Fahad Shahbaz Khan
,
Michael Felsberg
PDF
Cite
DOI
Supplementary
Code (unofficial)
Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking
CVPR 2016
A unified formulation for alleviating the problem of corrupted training samples in tracking-by-detection methods.
Martin Danelljan
,
Gustav Häger
,
Fahad Shahbaz Khan
,
Michael Felsberg
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Code
DOI
arXiv
Supplementary
Raw Results
Learning Spatially Regularized Correlation Filters for Visual Tracking
ICCV 2015
Mitigating the unwanted boundary effects, which limits the performance of correlation based trackers.
Martin Danelljan
,
Gustav Häger
,
Fahad Shahbaz Khan
,
Michael Felsberg
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Code
Project
Poster
DOI
arXiv
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