Probabilistic Trajectory Modeling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00170640" target="_blank" >RIV/68407700:21230/10:00170640 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Probabilistic Trajectory Modeling
Original language description
We introduce a new approach to trajectory modeling based on density estimation. First we estimate probability density function from all historian positions. To approximate this complicated distribution we use Gaussian mixture. Each trajectory can be thendescribed as a sequence of Gaussian distributions according to historical data. This sequence is used to learn hidden Markov model (HMM). When we have this model we can classify trajectories into different types, predict further behavior or detect anomalous trajectories.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
POSTER 2010 - Proceedings of the 14th International Conference on Electrical Engineering
ISBN
978-80-01-04544-2
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
ČVUT v Praze, FEL
Place of publication
Praha
Event location
Praha
Event date
May 6, 2010
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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