DAL: A Deep Depth-Aware 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%2F21%3A00354243" target="_blank" >RIV/68407700:21230/21:00354243 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICPR48806.2021.9412984" target="_blank" >https://doi.org/10.1109/ICPR48806.2021.9412984</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICPR48806.2021.9412984" target="_blank" >10.1109/ICPR48806.2021.9412984</a>
Alternative languages
Result language
angličtina
Original language name
DAL: A Deep Depth-Aware Long-term Tracker
Original language description
The best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets. In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 25th International Conference on Pattern Recognition (ICPR)
ISBN
978-1-7281-8808-9
ISSN
1051-4651
e-ISSN
1051-4651
Number of pages
8
Pages from-to
7825-7832
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Milan
Event date
Jan 10, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000681331400031