D3S - A discriminative single shot segmentation tracker
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00346885" target="_blank" >RIV/68407700:21230/20:00346885 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR42600.2020.00716" target="_blank" >https://doi.org/10.1109/CVPR42600.2020.00716</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR42600.2020.00716" target="_blank" >10.1109/CVPR42600.2020.00716</a>
Alternative languages
Result language
angličtina
Original language name
D3S - A discriminative single shot segmentation tracker
Original language description
Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of transformation models, which reduces their localization accuracy. We propose a discriminative single-shot segmentation tracker - D3S, which narrows the gap between visual object tracking and video object segmentation. A single-shot network applies two target models with complementary geometric properties, one invariant to a broad range of transformations, including non-rigid deformations, the other assuming a rigid object to simultaneously achieve high robustness and online target segmentation. Without per-dataset finetuning and trained only for segmentation as the primary output, D3S outperforms all trackers on VOT2016, VOT2018 and GOT-10k benchmarks and performs close to the state-of-the-art trackers on the TrackingNet. D3S outperforms the leading segmentation tracker SiamMask on video object segmentation benchmarks and performs on par with top video object segmentation algorithms, while running an order of magnitude faster, close to real-time.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-05360S" target="_blank" >GA18-05360S: Solving inverse problems for the analysis of fast moving objects</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition
ISBN
978-1-7281-7168-5
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
10
Pages from-to
7131-7140
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Seattle
Event date
Jun 13, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000620679507041