Sharing local information in scanning-window detection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00238014" target="_blank" >RIV/68407700:21230/15:00238014 - isvavai.cz</a>
Result on the web
<a href="http://cvww2015.icg.tugraz.at/papers_web/cvww2015_paper_id31.pdf" target="_blank" >http://cvww2015.icg.tugraz.at/papers_web/cvww2015_paper_id31.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3217/978-3-85125-388-7" target="_blank" >10.3217/978-3-85125-388-7</a>

Result language
angličtina
Original language name
Sharing local information in scanning-window detection
Original language description
WaldBoost algorithm is a state-of-the-art method for object detection due to its high detection accuracy and real-time speed. However, since the scanning window procedure does not make use of information shared among overlapping windows, there is still a possibility of a significant speed-up by exploiting this property. Zemcik et al. recently proposed to use a second classifier to suppress the neighboring positions with a negligible computational overhead. In this paper we improve upon the work of Zemcık et al. and show that with an improved scanning strategy and predictor selection we outperform it in both geometric accuracy as well as detection rate on the FDDB dataset for face detec- tion, while achieving the same or a higher speed-up
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
CVWW 2015: Proceedings of the 20th Computer Vision Winter Workshop
ISBN
978-3-85125-388-7
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
107-113
Publisher name
Graz University of Technology
Place of publication
Graz
Event location
Seggau
Event date
Feb 9, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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