Training Sequential On-line Boosting Classifier for Visual Tracking
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03150842" target="_blank" >RIV/68407700:21230/08:03150842 - 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
Training Sequential On-line Boosting Classifier for Visual Tracking
Original language description
On-line boosting allows to adapt a trained classifier to changing environmental conditions or to use sequentially available training data. Yet, two important problems in the on-line boosting training remain unsolved: (i) classifier evaluation speed optimization and, (ii) automatic classifier complexity estimation. In this paper we show how the on-line boosting can be combined with Wald's sequential decision theory to solve both of the problems.The properties of the proposed on-lineWaldBoost algorithm are demonstrated on a visual tracking problem. The complexity of the classifier is changing dynamically depending on the difficulty of the problem. On average, a speedup of a factor of 5-10 is achieved compared to the non-sequential on-line boosting.
Czech name
Training Sequential On-line Boosting Classifier for Visual Tracking
Czech description
On-line boosting allows to adapt a trained classifier to changing environmental conditions or to use sequentially available training data. Yet, two important problems in the on-line boosting training remain unsolved: (i) classifier evaluation speed optimization and, (ii) automatic classifier complexity estimation. In this paper we show how the on-line boosting can be combined with Wald's sequential decision theory to solve both of the problems.The properties of the proposed on-lineWaldBoost algorithm are demonstrated on a visual tracking problem. The complexity of the classifier is changing dynamically depending on the difficulty of the problem. On average, a speedup of a factor of 5-10 is achieved compared to the non-sequential on-line boosting.
Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA102%2F07%2F1317" target="_blank" >GA102/07/1317: Methods for Visual Recognition of Large Collections of Non-rigid Objects</a><br>
Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICPR 2008: Proceedings of the 19th International Conference on Pattern Recognition
ISBN
978-1-4244-2174-9
ISSN
1051-4651
e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
Omnipress
Place of publication
Madison
Event location
Tampa, Florida
Event date
Dec 8, 2008
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000264729000334