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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F11%3APU96174%21RIV12-MSM-26230___
rdf:type
n12:Vysledek skos:Concept
dcterms:description
The on-line human action detection is an important task in human-machine interaction and related applications. One of the possible approaches to the detection is exploitation of space-time interest points. Such points are typically identified using feature extractor and then they are processed and classifified. The classifification can be performed using codebooks built based on feature vectors statistics. The individual feature vectors are transformed into bag of words representation using such codebooks and then the code words are classified using SVM. The proposed approach improves the training process and extends the known approaches. The training part of the dataset is split into shorter shots with equal duration and these are annotated and classified using a SVM classifier. This ensures that the time-local motion is captured by the SVM while the longer time behavior is left on further processing mechanisms, such as, e.g. HMMs. In the proposed approach, the output of the SVM classifier is s The on-line human action detection is an important task in human-machine interaction and related applications. One of the possible approaches to the detection is exploitation of space-time interest points. Such points are typically identified using feature extractor and then they are processed and classifified. The classifification can be performed using codebooks built based on feature vectors statistics. The individual feature vectors are transformed into bag of words representation using such codebooks and then the code words are classified using SVM. The proposed approach improves the training process and extends the known approaches. The training part of the dataset is split into shorter shots with equal duration and these are annotated and classified using a SVM classifier. This ensures that the time-local motion is captured by the SVM while the longer time behavior is left on further processing mechanisms, such as, e.g. HMMs. In the proposed approach, the output of the SVM classifier is s
dcterms:title
On-line human action detection using space-time interest points On-line human action detection using space-time interest points
skos:prefLabel
On-line human action detection using space-time interest points On-line human action detection using space-time interest points
skos:notation
RIV/00216305:26230/11:PU96174!RIV12-MSM-26230___
n12:predkladatel
n13:orjk%3A26230
n3:aktivita
n5:P n5:Z
n3:aktivity
P(7E11024), Z(MSM0021630528)
n3:dodaniDat
n8:2012
n3:domaciTvurceVysledku
n6:9340386 n6:2326159
n3:druhVysledku
n20:D
n3:duvernostUdaju
n9:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
218368
n3:idVysledku
RIV/00216305:26230/11:PU96174
n3:jazykVysledku
n21:eng
n3:klicovaSlova
space-time interest points SVM classifier bag of words
n3:klicoveSlovo
n23:space-time%20interest%20points%20SVM%20classifier%20bag%20of%20words
n3:kontrolniKodProRIV
[0D05AF7E2541]
n3:mistoKonaniAkce
Hotel Boboty, Vrátna Dolina
n3:mistoVydani
Praha
n3:nazevZdroje
Zborník príspevkov prezentovaných na konferencii ITAT, september 2011
n3:obor
n22:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n10:7E11024
n3:rokUplatneniVysledku
n8:2011
n3:tvurceVysledku
Řezníček, Ivo Zemčík, Pavel
n3:typAkce
n15:EUR
n3:zahajeniAkce
2011-09-23+02:00
n3:zamer
n16:MSM0021630528
s:numberOfPages
7
n11:hasPublisher
Faculty of Mathematics and Physics
n7:isbn
978-80-89557-01-1
n18:organizacniJednotka
26230