FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00362995" target="_blank" >RIV/68407700:21230/22:00362995 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-20047-2_37" target="_blank" >https://doi.org/10.1007/978-3-031-20047-2_37</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-20047-2_37" target="_blank" >10.1007/978-3-031-20047-2_37</a>
Alternative languages
Result language
angličtina
Original language name
FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
Original language description
We present FEAR, a family of fast, efficient, accurate, and robust Siamese visual trackers. We present a novel and efficient way to benefit from dual-template representation for object model adaption, which incorporates temporal information with only a single learnable parameter. We further improve the tracker architecture with a pixel-wise fusion block. By plugging-in sophisticated backbones with the abovementioned modules, FEAR-M and FEAR-L trackers surpass most Siamese trackers on several academic benchmarks in both accuracy and efficiency. Employed with the lightweight backbone, the optimized version FEAR-XS offers more than 10 times faster tracking than current Siamese trackers while maintaining near state-of-the-art results. FEAR-XS tracker is 2.4x smaller and 4.3x faster than LightTrack with superior accuracy. In addition, we expand the definition of the model efficiency by introducing FEAR benchmark that assesses energy consumption and execution speed. We show that energy consumption is a limiting factor for trackers on mobile devices. Source code, pretrained models, and evaluation protocol are available at https://github.com/PinataFarms/FEARTracker.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
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/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)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computer Vision - ECCV 2022, Part XXII
ISBN
978-3-031-20046-5
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
20
Pages from-to
644-663
Publisher name
Springer, Cham
Place of publication
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Event location
Tel Aviv
Event date
Oct 23, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000904116000037