About: *Sharing local information in scanning-window detection

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Object detection is a classic task in computer vision. WaldBoost algorithm is a state-of-the-art method for object detection due its high detection accuracy and real-time speed. However, since the traditional scanning window method classifies all the windows independently and doesn't make use of the information shared among overlapping windows,there is still a possibility of a significant speed-up by exploiting this property. We evaluate number of scanning patterns and predictors for spatially adjacent windows, inspired by work of Hradiš et. al. Furthermore, we generalize this idea from spatially adjacent widows to multiple scales and propose {WaldBoost with Crosstalk Prediction}. Evaluating on a state-of-the-art dataset for face detection, we show that a significant speed-up can be achieved with {WaldBoost with Crosstalk Prediction} with no or a little loss of precision, outperforming the reference method of Hradiš et. al. Object detection is a classic task in computer vision. WaldBoost algorithm is a state-of-the-art method for object detection due its high detection accuracy and real-time speed. However, since the traditional scanning window method classifies all the windows independently and doesn't make use of the information shared among overlapping windows,there is still a possibility of a significant speed-up by exploiting this property. We evaluate number of scanning patterns and predictors for spatially adjacent windows, inspired by work of Hradiš et. al. Furthermore, we generalize this idea from spatially adjacent widows to multiple scales and propose {WaldBoost with Crosstalk Prediction}. Evaluating on a state-of-the-art dataset for face detection, we show that a significant speed-up can be achieved with {WaldBoost with Crosstalk Prediction} with no or a little loss of precision, outperforming the reference method of Hradiš et. al. (en)
Title	Sharing local information in scanning-window detection Sharing local information in scanning-window detection (en)
skos:prefLabel	Sharing local information in scanning-window detection Sharing local information in scanning-window detection (en)
skos:notation	RIV/68407700:21230/14:00223835!RIV15-MSM-21230___
http://linked.open...avai/riv/aktivita	N
http://linked.open...avai/riv/aktivity	N
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Pokorný, Jan Matas, Jiří Trefný, Jiří
http://linked.open.../riv/druhVysledku	V/S - Souhrnná výzkumná zpráva
http://linked.open...iv/duvernostUdaju	C - Předmět podléhá obchodnímu tajemství (§17 až 20 Obchodního zákoníku), ale některé údaje jsou upraveny tak, aby byly zveřejnitelné
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	44713
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/14:00223835
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Object detection; Sequential decison; WaldBoost; Scanning window (en)
http://linked.open.../riv/klicoveSlovo	Scanning window Sequential decison WaldBoost Object detection
http://linked.open...ontrolniKodProRIV	[F20CA8D732F2]
http://linked.open...i/riv/mistoVydani	Praha
http://linked.open...telVyzkumneZpravy	Toyota Motor Europe NV/SA TMEM
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Matas, Jiří Pokorný, Jan Trefný, Jiří
number of pages	11 (xsd:int)
http://localhost/t...ganizacniJednotka	21230

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