Similarity Searching in Long Sequences of Motion Capture Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00088023" target="_blank" >RIV/00216224:14330/16:00088023 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-46759-7_21" target="_blank" >http://dx.doi.org/10.1007/978-3-319-46759-7_21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-46759-7_21" target="_blank" >10.1007/978-3-319-46759-7_21</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Similarity Searching in Long Sequences of Motion Capture Data

Popis výsledku v původním jazyce

Motion capture data digitally represent human movements by sequences of body configurations in time. Searching in such spatio-temporal data is difficult as query-relevant motions can vary in lengths and occur arbitrarily in the very long data sequence. There is also a strong requirement on effective similarity comparison as the specific motion can be performed by various actors in different ways, speeds or starting positions. To deal with these problems, we propose a new subsequence matching algorithm which uses a synergy of elastic similarity measure and multi-level segmentation. The idea is to generate a minimum number of overlapping data segments so that there is at least one segment matching an arbitrary subsequence. A non-partitioned query is then efficiently evaluated by searching for the most similar segments in a single level only, while guaranteeing a precise answer with respect to the similarity measure.

Název v anglickém jazyce

Similarity Searching in Long Sequences of Motion Capture Data

Popis výsledku anglicky

Motion capture data digitally represent human movements by sequences of body configurations in time. Searching in such spatio-temporal data is difficult as query-relevant motions can vary in lengths and occur arbitrarily in the very long data sequence. There is also a strong requirement on effective similarity comparison as the specific motion can be performed by various actors in different ways, speeds or starting positions. To deal with these problems, we propose a new subsequence matching algorithm which uses a synergy of elastic similarity measure and multi-level segmentation. The idea is to generate a minimum number of overlapping data segments so that there is at least one segment matching an arbitrary subsequence. A non-partitioned query is then efficiently evaluated by searching for the most similar segments in a single level only, while guaranteeing a precise answer with respect to the similarity measure.

Druh

D - Stať ve sborníku

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 statě ve sborníku

Proceedings of 9th International Conference on Similarity Search and Applications (SISAP 2016), LNCS 9939

ISBN

9783319467580

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

15

Strana od-do

271-285

Název nakladatele

Springer International Publishing AG

Místo vydání

Cham (ZG)

Místo konání akce

Tokyo, Japan

Datum konání akce

1. 1. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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