A Discriminative Single-Shot Segmentation Network for Visual Object Tracking

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00362940" target="_blank" >RIV/68407700:21230/22:00362940 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TPAMI.2021.3137933" target="_blank" >https://doi.org/10.1109/TPAMI.2021.3137933</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2021.3137933" target="_blank" >10.1109/TPAMI.2021.3137933</a>

Result language
angličtina
Original language name
A Discriminative Single-Shot Segmentation Network for Visual Object Tracking
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(2), 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 robust online target segmentation. The overall tracking reliability is further increased by decoupling the object and feature scale estimation. Without per-dataset finetuning, and trained only for segmentation as the primary output, D3S(2) outperforms all published trackers on the recent short-term tracking benchmark VOT2020 and performs very close to the state-of-the-art trackers on the GOT-10k, TrackingNet, OTB100 and LaSoT. D3S(2) outperforms the leading segmentation tracker SiamMask on video object segmentation benchmarks and performs on par with top video object segmentation algorithms.
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
e-ISSN
1939-3539
Volume of the periodical
44
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
9742-9755
UT code for WoS article
000880661400085
EID of the result in the Scopus database
2-s2.0-85122070980

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