Message Passing Algorithms for Scalable Multitarget Tracking
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU130638" target="_blank" >RIV/00216305:26220/18:PU130638 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/JPROC.2018.2789427" target="_blank" >http://dx.doi.org/10.1109/JPROC.2018.2789427</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JPROC.2018.2789427" target="_blank" >10.1109/JPROC.2018.2789427</a>
Alternative languages
Result language
angličtina
Original language name
Message Passing Algorithms for Scalable Multitarget Tracking
Original language description
Situation-aware technologies enabled by multitarget tracking will lead to new services and applications in fields such as autonomous driving, indoor localization, robotic networks, and crowd counting. In this tutorial paper, we advocate a recently proposed paradigm for scalable multitarget tracking that is based on message passing or, more concretely, the loopy sum-product algorithm. This approach has advantages regarding estimation accuracy, computational complexity, and implementation flexibility. Most importantly, it provides a highly effective, efficient, and scalable solution to the probabilistic data association problem, a major challenge in multitarget tracking. This fact makes it attractive for emerging applications requiring real-time operation on resource-limited devices. In addition, the message passing approach is intuitively appealing and suited to nonlinear and non-Gaussian models.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/GA17-19638S" target="_blank" >GA17-19638S: Sequential Bayesian Estimation of Arterial Wall Motion</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
Name of the periodical
PROCEEDINGS OF THE IEEE
ISSN
0018-9219
e-ISSN
1558-2256
Volume of the periodical
106
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
39
Pages from-to
221-259
UT code for WoS article
000425055600002
EID of the result in the Scopus database
2-s2.0-85042004490