This HTML5 document contains 44 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n5http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n20http://localhost/temp/predkladatel/
n14http://purl.org/net/nknouf/ns/bibtex#
n21http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n9http://linked.opendata.cz/resource/domain/vavai/projekt/
n19http://linked.opendata.cz/ontology/domain/vavai/
n16https://schema.org/
n10http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21230%2F10%3A00175502%21RIV11-GA0-21230___/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n12http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n4http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n11http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n8http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n17http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n15http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n6http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F10%3A00175502%21RIV11-GA0-21230___
rdf:type
skos:Concept n19:Vysledek
dcterms:description
Loss-of-track detection (tracking validation) and automatic tracker adaptation to new object appearances are attractive topics in computer vision. We apply very efficient learnable sequential predictors in order to address both issues. Validation is done by clustering of the sequential predictor responses. No aditional object model for validation is needed. The paper also proposes an incremental learning procedure that accommodates changing object appearance, which mainly improves the recall of the tracker/detector. Exemplars for the incremental learning are collected automatically, no user interaction is required. The aditional training examples are selected automatically using the tracker stability computed for each potential aditional training example. Coupled with a sparsely applied SIFT or SURF based detector the method is employed for object localization in videos. Our Matlab implementation scans videosequences up to eight times faster than the actual frame rate. A standard-lengt Loss-of-track detection (tracking validation) and automatic tracker adaptation to new object appearances are attractive topics in computer vision. We apply very efficient learnable sequential predictors in order to address both issues. Validation is done by clustering of the sequential predictor responses. No aditional object model for validation is needed. The paper also proposes an incremental learning procedure that accommodates changing object appearance, which mainly improves the recall of the tracker/detector. Exemplars for the incremental learning are collected automatically, no user interaction is required. The aditional training examples are selected automatically using the tracker stability computed for each potential aditional training example. Coupled with a sparsely applied SIFT or SURF based detector the method is employed for object localization in videos. Our Matlab implementation scans videosequences up to eight times faster than the actual frame rate. A standard-lengt
dcterms:title
Incremental learning and validation of sequential predictors in video browsing application Incremental learning and validation of sequential predictors in video browsing application
skos:prefLabel
Incremental learning and validation of sequential predictors in video browsing application Incremental learning and validation of sequential predictors in video browsing application
skos:notation
RIV/68407700:21230/10:00175502!RIV11-GA0-21230___
n3:aktivita
n8:P
n3:aktivity
P(GAP103/10/1585)
n3:dodaniDat
n6:2011
n3:domaciTvurceVysledku
n21:9397000 n21:1144359
n3:druhVysledku
n17:D
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
263389
n3:idVysledku
RIV/68407700:21230/10:00175502
n3:jazykVysledku
n11:eng
n3:klicovaSlova
predictors; incremental; learning; validation; video; browsing
n3:klicoveSlovo
n12:browsing n12:incremental n12:learning n12:validation n12:video n12:predictors
n3:kontrolniKodProRIV
[CE58D337A798]
n3:mistoKonaniAkce
Angers
n3:mistoVydani
Setúbal
n3:nazevZdroje
VISIGRAPP 2010: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
n3:obor
n15:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n9:GAP103%2F10%2F1585
n3:rokUplatneniVysledku
n6:2010
n3:tvurceVysledku
Svoboda, Tomáš Hurych, David
n3:typAkce
n5:WRD
n3:zahajeniAkce
2010-05-17+02:00
s:numberOfPages
8
n14:hasPublisher
Institute for Systems and Technologies of Information, Control and Communication
n16:isbn
978-989-674-028-3
n20:organizacniJednotka
21230