On-line human action detection using space-time interest points

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F11%3APU96174" target="_blank" >RIV/00216305:26230/11:PU96174 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

On-line human action detection using space-time interest points

Popis výsledku v původním jazyce

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 class

Název v anglickém jazyce

On-line human action detection using space-time interest points

Popis výsledku anglicky

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 class

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/7E11024" target="_blank" >7E11024: Together Anywhere, Together Anytime - Enlarged European Union</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Zborník príspevkov prezentovaných na konferencii ITAT, september 2011

ISBN

978-80-89557-01-1

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

39-45

Název nakladatele

Faculty of Mathematics and Physics

Místo vydání

Praha

Místo konání akce

Hotel Boboty, Vrátna Dolina

Datum konání akce

23. 9. 2011

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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