Discriminative Correlation Filter Tracker with Channel and Spatial Reliability

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>

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.

Druh

J_imp - 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)

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)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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