Discriminative Correlation Filter with Channel and Spatial Reliability

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315732" target="_blank" >RIV/68407700:21230/17:00315732 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CVPR.2017.515" target="_blank" >http://dx.doi.org/10.1109/CVPR.2017.515</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR.2017.515" target="_blank" >10.1109/CVPR.2017.515</a>

Result language
angličtina
Original language name
Discriminative Correlation Filter with Channel and Spatial Reliability
Original language description
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 novel 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 allows tracking of non-rectangular objects as well as extending the search region. Channel reliability reflects the quality of the learned filter and it is used as a feature weighting coefficient in localization. Experimentally, with only two simple standard features, 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 OTB. The CSR-DCF runs in real-time on a CPU.
Czech name
—
Czech description
—

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)

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)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
CVPR 2017: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition
ISBN
978-1-5386-0457-1
ISSN
1063-6919
e-ISSN
—
Number of pages
10
Pages from-to
4847-4856
Publisher name
IEEE Computer Society Press
Place of publication
—
Event location
Honolulu
Event date
Jul 21, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000418371404099

Similar results(10)