FuCoLoT – A Fully-Correlational Long-Term Tracker
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00337393" target="_blank" >RIV/68407700:21230/19:00337393 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-20890-5_38" target="_blank" >http://dx.doi.org/10.1007/978-3-030-20890-5_38</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-20890-5_38" target="_blank" >10.1007/978-3-030-20890-5_38</a>
Alternative languages
Result language
angličtina
Original language name
FuCoLoT – A Fully-Correlational Long-Term Tracker
Original language description
We propose FuCoLoT – a Fully Correlational Long-term Tracker. It exploits the novel DCF constrained filter learning method to design a detector that is able to re-detect the target in the whole image efficiently. FuCoLoT maintains several correlation filters trained on different time scales that act as the detector components. A novel mechanism based on the correlation response is used for tracking failure estimation. FuCoLoT achieves state-of-the-art results on standard short-term benchmarks and it outperforms the current best-performing tracker on the long-term UAV20L benchmark by over 19%. It has an order of magnitude smaller memory footprint than its best-performing competitors and runs at 15Â fps in a single CPU thread.
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/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ACCV 2018: Proceedings of the 14th Asian Conference on Computer Vision, Part II
ISBN
978-3-030-20889-9
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
17
Pages from-to
595-611
Publisher name
Springer
Place of publication
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Event location
Perth
Event date
Dec 4, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000492902300038