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Alvaro Garcia-Martin

Researcher at Autonomous University of Madrid

Publications -  38
Citations -  2453

Alvaro Garcia-Martin is an academic researcher from Autonomous University of Madrid. The author has contributed to research in topics: Video tracking & Computer science. The author has an hindex of 12, co-authored 33 publications receiving 1828 citations. Previous affiliations of Alvaro Garcia-Martin include University of Extremadura.

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Book ChapterDOI

The Visual Object Tracking VOT2016 Challenge Results

Matej Kristan, +140 more
TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
Book ChapterDOI

The sixth visual object tracking VOT2018 challenge results

Matej Kristan, +158 more
TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Proceedings ArticleDOI

The Visual Object Tracking VOT2017 Challenge Results

Matej Kristan, +104 more
TL;DR: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative; results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years.
Book ChapterDOI

The Eighth Visual Object Tracking VOT2020 Challenge Results

Matej Kristan, +109 more
TL;DR: A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
Book ChapterDOI

The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results

Michael Felsberg, +76 more
TL;DR: The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance.