About: Exploiting Features - Locally Interleaved Sequential Alignment for Object 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)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	ftp://cmp.felk.cvut.cz/pub/cmp/articles/hurycd1/hurych-accv2012.pdf
Description	We exploit image features multiple times in order to make sequential decision process faster and better performing. In the decision process features providing knowledge about the object presence or absence in a given detection window are successively evaluated. We show that these features also provide information about object position within the evaluated window. The classification process is sequentially interleaved with estimating the correct position. The position estimate is used for steering the features yet to be evaluated. This locally interleaved sequential alignment (LISA) allows to run an object detector on sparser grid which speeds up the process. The position alignment is jointly learned with the detector. We achieve a better detection rate since the method allows for training the detector on perfectly aligned image samples. For estimation of the alignment we propose a learnable regressor that approximates a non-linear regression function and runs in ne2076-1465gligible time. We exploit image features multiple times in order to make sequential decision process faster and better performing. In the decision process features providing knowledge about the object presence or absence in a given detection window are successively evaluated. We show that these features also provide information about object position within the evaluated window. The classification process is sequentially interleaved with estimating the correct position. The position estimate is used for steering the features yet to be evaluated. This locally interleaved sequential alignment (LISA) allows to run an object detector on sparser grid which speeds up the process. The position alignment is jointly learned with the detector. We achieve a better detection rate since the method allows for training the detector on perfectly aligned image samples. For estimation of the alignment we propose a learnable regressor that approximates a non-linear regression function and runs in ne2076-1465gligible time. (en)
Title	Exploiting Features - Locally Interleaved Sequential Alignment for Object Detection Exploiting Features - Locally Interleaved Sequential Alignment for Object Detection (en)
skos:prefLabel	Exploiting Features - Locally Interleaved Sequential Alignment for Object Detection Exploiting Features - Locally Interleaved Sequential Alignment for Object Detection (en)
skos:notation	RIV/68407700:21230/13:00212523!RIV15-MSM-21230___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(7E10044), P(GAP103/10/1585), P(GPP103/11/P700)
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Zimmermann, Karel Svoboda, Tomáš Hurych, David
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	74294
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/13:00212523
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	exploit; features; adaboost; waldboost; sequential; regression; predictors; interleaved; alignment; sliding window (en)
http://linked.open.../riv/klicoveSlovo	features sliding window alignment predictors adaboost exploit interleaved waldboost sequential regression
http://linked.open...ontrolniKodProRIV	[BFDD0BB9596E]
http://linked.open...v/mistoKonaniAkce	Daejeon
http://linked.open...i/riv/mistoVydani	Heidelberg
http://linked.open...i/riv/nazevZdroje	Computer Vision - ACCV 2012, 11th Asian Conference on Computer Vision, Part 1
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...vavai/riv/projekt	Advanced predictors for object detection and tracking in video Application of regression methods for tracking of unknown objects Natural human-robot cooperation in dynamic environments
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Svoboda, Tomáš Zimmermann, Karel Hurych, David
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-11-05 (xsd:date)
issn	0302-9743
number of pages	14 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-642-37331-2_34
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-642-37330-5
http://localhost/t...ganizacniJednotka	21230

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