Discriminative Correlation Filter Tracker with Channel and Spatial Reliability
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00318711" target="_blank" >RIV/68407700:21230/18:00318711 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1007/s11263-017-1061-3" target="_blank" >https://doi.org/10.1007/s11263-017-1061-3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11263-017-1061-3" target="_blank" >10.1007/s11263-017-1061-3</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Discriminative Correlation Filter Tracker with Channel and Spatial Reliability
Popis výsledku v původním jazyce
Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard feature sets, HoGs and colornames, the novel CSR-DCF method---DCF with channel and spatial reliability---achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs close to real-time on a CPU.
Název v anglickém jazyce
Discriminative Correlation Filter Tracker with Channel and Spatial Reliability
Popis výsledku anglicky
Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard feature sets, HoGs and colornames, the novel CSR-DCF method---DCF with channel and spatial reliability---achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs close to real-time on a CPU.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2018
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
International Journal of Computer Vision
ISSN
0920-5691
e-ISSN
1573-1405
Svazek periodika
126
Číslo periodika v rámci svazku
7
Stát vydavatele periodika
NL - Nizozemsko
Počet stran výsledku
18
Strana od-do
671-688
Kód UT WoS článku
000433072800001
EID výsledku v databázi Scopus
2-s2.0-85047409502