Assessing similarity models for human-motion retrieval applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00087724" target="_blank" >RIV/00216224:14330/16:00087724 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1002/cav.1674" target="_blank" >http://dx.doi.org/10.1002/cav.1674</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cav.1674" target="_blank" >10.1002/cav.1674</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Assessing similarity models for human-motion retrieval applications

Popis výsledku v původním jazyce

The development of motion capturing devices poses new challenges in the exploitation of human-motion data for various application fields, such as computer animation, visual surveillance, sports or physical medicine. Recently, a number of approaches dealing with motion data have been proposed, suggesting characteristic motion features to be extracted and compared on the basis of similarity. Unfortunately, almost each approach defines its own set of motion features and comparison methods, thus it is hard to fairly decide which similarity model is the most suitable for a given kind of human-motion retrieval application. To cope with this problem, we propose the HumAn Motion Model EvaluatoR (HAMMER) which is a generic framework for assessing candidate similarity models with respect to the purpose of the target application. The application purpose is specified by a user in form of a representative sample of categorized motion data.

Název v anglickém jazyce

Assessing similarity models for human-motion retrieval applications

Popis výsledku anglicky

The development of motion capturing devices poses new challenges in the exploitation of human-motion data for various application fields, such as computer animation, visual surveillance, sports or physical medicine. Recently, a number of approaches dealing with motion data have been proposed, suggesting characteristic motion features to be extracted and compared on the basis of similarity. Unfortunately, almost each approach defines its own set of motion features and comparison methods, thus it is hard to fairly decide which similarity model is the most suitable for a given kind of human-motion retrieval application. To cope with this problem, we propose the HumAn Motion Model EvaluatoR (HAMMER) which is a generic framework for assessing candidate similarity models with respect to the purpose of the target application. The application purpose is specified by a user in form of a representative sample of categorized motion data.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Computer Animation and Virtual Worlds

ISSN

1546-4261

e-ISSN

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Svazek periodika

27

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

484-500

Kód UT WoS článku

000385614100003

EID výsledku v databázi Scopus

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