Depth Masked Discriminative Correlation Filter
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00327978" target="_blank" >RIV/68407700:21230/18:00327978 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICPR.2018.8546179" target="_blank" >http://dx.doi.org/10.1109/ICPR.2018.8546179</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICPR.2018.8546179" target="_blank" >10.1109/ICPR.2018.8546179</a>
Alternative languages
Result language
angličtina
Original language name
Depth Masked Discriminative Correlation Filter
Original language description
Depth information provides a strong cue for occlusion detection and handling, but has been largely omitted in generic object tracking until recently due to lack of suitable benchmark datasets and applications. In this work, we propose a Depth Masked Discriminative Correlation Filter (DM-DCF) which adopts novel depth segmentation based occlusion detection that stops correlation filter updating and depth masking which adaptively adjusts the spatial support for correlation filter. In Princeton RGBD Tracking Benchmark, our DM-DCF is among the state-of-the-art in overall ranking and the winner on multiple categories. Moreover, since it is based on DCF, "DM-DCF" runs an order of magnitude faster than its competitors making it suitable for time constrained applications.
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
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2018 24rd International Conference on Pattern Recognition (ICPR)
ISBN
978-1-5386-3788-3
ISSN
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e-ISSN
1051-4651
Number of pages
6
Pages from-to
2112-2117
Publisher name
IEEE
Place of publication
Piscataway, NJ
Event location
Beijing
Event date
Aug 20, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000455146802021